Triple
T19933266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gian Diego Tipaldi |
E479109
|
entity |
| Predicate | coAuthorWith |
P398
|
FINISHED |
| Object | Cyrill Stachniss |
—
|
NE NERFINISHED |
How this triple was built (2 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: Cyrill Stachniss | Statement: [Gian Diego Tipaldi, coAuthorWith, Cyrill Stachniss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cyrill Stachniss Context triple: [Gian Diego Tipaldi, coAuthorWith, Cyrill Stachniss]
-
A.
Cyrill Stachniss
chosen
Cyrill Stachniss is a German computer scientist and robotics researcher known for his work in mobile robotics, SLAM, and machine perception.
-
B.
Wolfram Burgard
Wolfram Burgard is a German computer scientist and roboticist known for his influential work in probabilistic robotics, autonomous navigation, and artificial intelligence.
-
C.
Bernd Girod
Bernd Girod is a German-American electrical engineer and computer scientist known for his influential work in video compression, multimedia signal processing, and visual communication.
-
D.
Martin Riedmiller
Martin Riedmiller is a German computer scientist and pioneer in deep reinforcement learning, known for his influential work on neural-network-based control and contributions to landmark deep RL systems.
-
E.
Andrew Zisserman
Andrew Zisserman is a prominent British computer vision researcher and professor known for foundational contributions to object recognition, image understanding, and influential deep learning architectures.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e521855c8190b41871700afc8d6a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65a1553348190a6c4004d3f9a57c5 |
completed | April 20, 2026, 4:53 p.m. |
Created at: April 10, 2026, 1:53 p.m.