Triple

T31541426
Position Surface form Disambiguated ID Type / Status
Subject 3rd Battalion, Royal 22e Régiment E804756 entity
Predicate alsoKnownAs P39 FINISHED
Object 3 R22eR
3 R22eR is the 3rd Battalion of the Royal 22e Régiment, a francophone infantry unit of the Canadian Army.
E1967193 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: 3 R22eR | Statement: [3rd Battalion, Royal 22e Régiment, alsoKnownAs, 3 R22eR]
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: 3 R22eR
Triple: [3rd Battalion, Royal 22e Régiment, alsoKnownAs, 3 R22eR]
Generated description
3 R22eR is the 3rd Battalion of the Royal 22e Régiment, a francophone infantry unit of the Canadian Army.

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_69f348d11a048190a65eb8384a3754ac completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a7a51e088190ad489201654477c4 completed May 3, 2026, 1:40 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b2d88324c81909b3df10eed98e6bf completed June 11, 2026, 9:50 p.m.
NEDg Description generation batch_6a2b2f6422e08190a882349b6f9204a9 completed June 11, 2026, 9:57 p.m.
NED2 Entity disambiguation (via description) batch_6a2b2fec9a348190bdefb8e7747f7ef9 completed June 11, 2026, 10 p.m.
Created at: April 30, 2026, 10:06 p.m.