Triple

T27357585
Position Surface form Disambiguated ID Type / Status
Subject 14th Brigade E685726 entity
Predicate notableCommander P1197 FINISHED
Object Brigadier James Hargest
Brigadier James Hargest was a New Zealand military officer and politician best known for his leadership of New Zealand forces in both World Wars and his service as a Member of Parliament.
E1776109 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: Brigadier James Hargest | Statement: [14th Brigade, notableCommander, Brigadier James Hargest]
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: Brigadier James Hargest
Triple: [14th Brigade, notableCommander, Brigadier James Hargest]
Generated description
Brigadier James Hargest was a New Zealand military officer and politician best known for his leadership of New Zealand forces in both World Wars and his service as a Member of Parliament.

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_69ef14887c288190931b8431fdbf53c4 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f62c20321481908ba574ad96d93013 completed May 2, 2026, 4:53 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12bbca05fc8190af7c7c243b23fee9 completed May 24, 2026, 8:50 a.m.
NEDg Description generation batch_6a12bd0d87a88190a617ee64551f7d93 completed May 24, 2026, 8:55 a.m.
NED2 Entity disambiguation (via description) batch_6a12be3a604c8190887660a427cd9f2f completed May 24, 2026, 9 a.m.
Created at: April 27, 2026, 11:52 a.m.