Triple

T34337581
Position Surface form Disambiguated ID Type / Status
Subject Interview magazine E881192 entity
Predicate notableContributor P304 FINISHED
Object Bob Colacello
Bob Colacello is an American writer and editor best known for his close collaboration with Andy Warhol and his influential work chronicling New York’s art, fashion, and celebrity scenes.
 E2092029 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: Bob Colacello | Statement: [Interview magazine, notableContributor, Bob Colacello]
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: Bob Colacello
Triple: [Interview magazine, notableContributor, Bob Colacello]
Generated description
Bob Colacello is an American writer and editor best known for his close collaboration with Andy Warhol and his influential work chronicling New York’s art, fashion, and celebrity scenes.

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_69f349ba96a08190b94887bae2d8ee49 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f713c503b88190943eea58864534fa completed May 3, 2026, 9:22 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36f9ddacbc8190aca135d9c16b8ee1 completed June 20, 2026, 8:36 p.m.
NEDg Description generation batch_6a36fbbcbcfc8190a54e28379529cc6f completed June 20, 2026, 8:44 p.m.
NED2 Entity disambiguation (via description) batch_6a36fc2b7ad8819080f589c461aa960a completed June 20, 2026, 8:46 p.m.
Created at: May 1, 2026, 1:58 a.m.