Triple

T35654016
Position Surface form Disambiguated ID Type / Status
Subject John Gossage E1030232 entity
Predicate notableWork P4 FINISHED
Object She Called Me by Name
She Called Me by Name is a photobook by American photographer John Gossage, known for its intimate, atmospheric black-and-white images and subtle narrative approach.
 E2149904 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: She Called Me by Name | Statement: [John Gossage, notableWork, She Called Me by Name]
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: She Called Me by Name
Triple: [John Gossage, notableWork, She Called Me by Name]
Generated description
She Called Me by Name is a photobook by American photographer John Gossage, known for its intimate, atmospheric black-and-white images and subtle narrative approach.

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_69f76e0938088190a8f199631e97dec3 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f79f769dec81908e0e638851ac83dd completed May 3, 2026, 7:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38685f14288190b63beca6ad255fee completed June 21, 2026, 10:40 p.m.
NEDg Description generation batch_6a38694064788190a60dcb80c9c8033a completed June 21, 2026, 10:44 p.m.
NED2 Entity disambiguation (via description) batch_6a3869d593388190a015a87a400f2205 completed June 21, 2026, 10:46 p.m.
Created at: May 3, 2026, 4:05 p.m.