Triple
T4293699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A3C |
E99656
|
entity |
| Predicate | inspiredAlgorithms |
P42754
|
FINISHED |
| Object |
IMPALA
IMPALA is a scalable deep reinforcement learning architecture designed for efficient distributed training of agents across many tasks and environments.
|
E428323
|
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: IMPALA | Statement: [A3C, inspiredAlgorithms, IMPALA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: IMPALA Context triple: [A3C, inspiredAlgorithms, IMPALA]
-
A.
Apache Hive
Apache Hive is a data warehouse and SQL-like query system built on top of Hadoop for managing and analyzing large datasets stored in distributed storage.
-
B.
Presto
Presto is an open-source, distributed SQL query engine designed for fast, interactive analytics on large-scale data from multiple sources.
-
C.
Presto
Presto is a discontinued proprietary browser engine developed by Opera Software that powered older versions of the Opera web browser.
-
D.
Apache Pig
Apache Pig is a high-level platform for creating MapReduce programs used to analyze large data sets in the Hadoop ecosystem.
-
E.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
- 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: IMPALA Triple: [A3C, inspiredAlgorithms, IMPALA]
Generated description
IMPALA is a scalable deep reinforcement learning architecture designed for efficient distributed training of agents across many tasks and environments.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: IMPALA Target entity description: IMPALA is a scalable deep reinforcement learning architecture designed for efficient distributed training of agents across many tasks and environments.
-
A.
Apache Hive
Apache Hive is a data warehouse and SQL-like query system built on top of Hadoop for managing and analyzing large datasets stored in distributed storage.
-
B.
Presto
Presto is an open-source, distributed SQL query engine designed for fast, interactive analytics on large-scale data from multiple sources.
-
C.
Presto
Presto is a discontinued proprietary browser engine developed by Opera Software that powered older versions of the Opera web browser.
-
D.
Apache Pig
Apache Pig is a high-level platform for creating MapReduce programs used to analyze large data sets in the Hadoop ecosystem.
-
E.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
- 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_69b3455175088190aa79c6e03b86647e |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35082228081908504e3fd7c4ca1e8 |
completed | March 12, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5c73d47448190a844bc13eae84a54 |
completed | March 14, 2026, 8:38 p.m. |
| NEDg | Description generation | batch_69b5c7d04508819087b14c5c86f1e015 |
completed | March 14, 2026, 8:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5c84ccea08190a8e7e8fa93934ea2 |
completed | March 14, 2026, 8:42 p.m. |
Created at: March 12, 2026, 11:08 p.m.