Triple

T31647269
Position Surface form Disambiguated ID Type / Status
Subject 500th MI Brigade E807613 entity
Predicate subordinateUnit P258 FINISHED
Object 301st Military Intelligence Battalion
The 301st Military Intelligence Battalion is a U.S. Army unit specializing in intelligence collection, analysis, and support to military operations.
E1972736 NE FINISHED

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: 301st Military Intelligence Battalion | Statement: [500th MI Brigade, subordinateUnit, 301st Military Intelligence Battalion]
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: 301st Military Intelligence Battalion
Triple: [500th MI Brigade, subordinateUnit, 301st Military Intelligence Battalion]
Generated description
The 301st Military Intelligence Battalion is a U.S. Army unit specializing in intelligence collection, analysis, and support to military operations.

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_69f348d9ce58819093ea2da83cbeeec1 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a956e9b08190bf83547bba8e8147 completed May 3, 2026, 1:48 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b84ad10048190b31974ddc4aad93f completed June 12, 2026, 4:01 a.m.
NEDg Description generation batch_6a2b8566b67c8190849d33decd4d64c5 completed June 12, 2026, 4:04 a.m.
NED2 Entity disambiguation (via description) batch_6a2b86252e808190a55351b93217d5f8 completed June 12, 2026, 4:08 a.m.
Created at: April 30, 2026, 10:51 p.m.