Triple
T4650850
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Probabilistic Robotics |
E102290
|
entity |
| Predicate | topic |
P261
|
FINISHED |
| Object |
Monte Carlo localization
Monte Carlo localization is a probabilistic robotics algorithm that uses particle filters to estimate a robot’s pose within a known map based on noisy sensor and motion data.
|
E457844
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Monte Carlo localization | Statement: [Probabilistic Robotics, topic, Monte Carlo localization]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monte Carlo localization Context triple: [Probabilistic Robotics, topic, Monte Carlo localization]
-
A.
SLAM
SLAM is a major art museum in St. Louis, Missouri, renowned for its extensive collection spanning thousands of years and diverse cultures.
-
B.
CMU Highly Intelligent Mobile Platform
CMU Highly Intelligent Mobile Platform (CHIMP) is a sophisticated humanoid robot developed at Carnegie Mellon University for advanced mobility, manipulation, and autonomous operation in challenging environments.
-
C.
book "Probabilistic Robotics"
"Probabilistic Robotics" is a foundational textbook that systematically introduces probabilistic methods for perception, localization, and control in mobile robotics.
-
D.
Learning to See by Moving
"Learning to See by Moving" is a research work in computer vision that explores how visual understanding can emerge from an agent’s own movement and interaction with the environment, rather than from static images alone.
-
E.
Technical Committee on Mobile Robots
The Technical Committee on Mobile Robots is a specialized IEEE Robotics and Automation Society group that advances research, standards, and collaboration in autonomous and mobile robotics.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Monte Carlo localization Triple: [Probabilistic Robotics, topic, Monte Carlo localization]
Generated description
Monte Carlo localization is a probabilistic robotics algorithm that uses particle filters to estimate a robot’s pose within a known map based on noisy sensor and motion data.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Monte Carlo localization Target entity description: Monte Carlo localization is a probabilistic robotics algorithm that uses particle filters to estimate a robot’s pose within a known map based on noisy sensor and motion data.
-
A.
SLAM
SLAM is a major art museum in St. Louis, Missouri, renowned for its extensive collection spanning thousands of years and diverse cultures.
-
B.
CMU Highly Intelligent Mobile Platform
CMU Highly Intelligent Mobile Platform (CHIMP) is a sophisticated humanoid robot developed at Carnegie Mellon University for advanced mobility, manipulation, and autonomous operation in challenging environments.
-
C.
book "Probabilistic Robotics"
"Probabilistic Robotics" is a foundational textbook that systematically introduces probabilistic methods for perception, localization, and control in mobile robotics.
-
D.
Learning to See by Moving
"Learning to See by Moving" is a research work in computer vision that explores how visual understanding can emerge from an agent’s own movement and interaction with the environment, rather than from static images alone.
-
E.
Technical Committee on Mobile Robots
The Technical Committee on Mobile Robots is a specialized IEEE Robotics and Automation Society group that advances research, standards, and collaboration in autonomous and mobile robotics.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69bd43d71a308190afea7280841b0de8 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd6302078081909451589d39c7b28c |
completed | March 20, 2026, 3:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfae7636881908244b86cba1c66b7 |
completed | March 21, 2026, 1:56 a.m. |
| NEDg | Description generation | batch_69bdfbc12acc8190b8116a6003abb3e3 |
completed | March 21, 2026, 2 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bdfc44536c8190a71e52b0690a7570 |
completed | March 21, 2026, 2:02 a.m. |
Created at: March 20, 2026, 1:14 p.m.