Triple

T28430490
Position Surface form Disambiguated ID Type / Status
Subject Bill Barber E715112 entity
Predicate fullName P16 FINISHED
Object John William Barber
John William Barber, better known as Bill Barber, was a Canadian Hall of Fame left winger who starred for the Philadelphia Flyers during the 1970s, helping them win two Stanley Cups.
 E1818048 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: John William Barber | Statement: [Bill Barber, fullName, John William Barber]
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: John William Barber
Triple: [Bill Barber, fullName, John William Barber]
Generated description
John William Barber, better known as Bill Barber, was a Canadian Hall of Fame left winger who starred for the Philadelphia Flyers during the 1970s, helping them win two Stanley Cups.

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_69efd6b253888190b3c7222ed6a403a8 completed April 27, 2026, 9:35 p.m.
NER Named-entity recognition batch_69f64e00a9208190b81350c4eccb0d12 completed May 2, 2026, 7:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1633242a888190adcbc87235144fa0 completed May 26, 2026, 11:56 p.m.
NEDg Description generation batch_6a16371a84748190b30e20a732ccb519 completed May 27, 2026, 12:13 a.m.
NED2 Entity disambiguation (via description) batch_6a1637e4d0548190b8d4c7a90580032d completed May 27, 2026, 12:16 a.m.
Created at: April 28, 2026, 1:39 a.m.