Triple

T37213810
Position Surface form Disambiguated ID Type / Status
Subject Arthur Elgort E922677 entity
Predicate notablePublication P4 FINISHED
Object Arthur Elgort’s Models Manual
Arthur Elgort’s Models Manual is a photography book by fashion photographer Arthur Elgort that offers behind-the-scenes insights, images, and practical guidance on working with models in the fashion industry.
 E2217870 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: Arthur Elgort’s Models Manual | Statement: [Arthur Elgort, notablePublication, Arthur Elgort’s Models Manual]
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: Arthur Elgort’s Models Manual
Triple: [Arthur Elgort, notablePublication, Arthur Elgort’s Models Manual]
Generated description
Arthur Elgort’s Models Manual is a photography book by fashion photographer Arthur Elgort that offers behind-the-scenes insights, images, and practical guidance on working with models in the fashion industry.

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_69f76ea6f5288190b8d9988f613811c0 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb367480b88190909a2517861bedfc completed May 6, 2026, 12:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a4036273d648190b759f22611d99514 completed June 27, 2026, 8:44 p.m.
NEDg Description generation batch_6a40385a12d481908e3723ff451ffe92 completed June 27, 2026, 8:53 p.m.
NED2 Entity disambiguation (via description) batch_6a403905066c8190997af99b48b21a75 completed June 27, 2026, 8:56 p.m.
Created at: May 3, 2026, 4:15 p.m.