Triple

T24471324
Position Surface form Disambiguated ID Type / Status
Subject 1st Battalion, 320th Artillery Regiment E617108 entity
Predicate partOf P40 FINISHED
Object 320th Field Artillery Regiment
The 320th Field Artillery Regiment is a U.S. Army artillery unit with a long history of providing fire support to airborne and infantry forces in major conflicts.
E1636225 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: 320th Field Artillery Regiment | Statement: [1st Battalion, 320th Artillery Regiment, partOf, 320th Field Artillery Regiment]
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: 320th Field Artillery Regiment
Triple: [1st Battalion, 320th Artillery Regiment, partOf, 320th Field Artillery Regiment]
Generated description
The 320th Field Artillery Regiment is a U.S. Army artillery unit with a long history of providing fire support to airborne and infantry forces in major conflicts.

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_69e2d7f197588190889a03e620558059 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f29943e9cc81909f1742c778bf3587 completed April 29, 2026, 11:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fe3922e708190abd9672b0f47bfef completed May 22, 2026, 5:03 a.m.
NEDg Description generation batch_6a0fe6c7cf0c8190a519141e2d729f8a completed May 22, 2026, 5:16 a.m.
NED2 Entity disambiguation (via description) batch_6a0fe76385488190991e3bd99cd9d9e9 completed May 22, 2026, 5:19 a.m.
Created at: April 18, 2026, 2:20 a.m.