Triple

T23868950
Position Surface form Disambiguated ID Type / Status
Subject Chris Steele-Perkins E592664 entity
Predicate notableWork P4 FINISHED
Object Fading Light: Portraits of Centenarians
Fading Light: Portraits of Centenarians is a photographic book that presents intimate, dignified portraits and life stories of people aged 100 and over.
E1603879 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: Fading Light: Portraits of Centenarians | Statement: [Chris Steele-Perkins, notableWork, Fading Light: Portraits of Centenarians]
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: Fading Light: Portraits of Centenarians
Triple: [Chris Steele-Perkins, notableWork, Fading Light: Portraits of Centenarians]
Generated description
Fading Light: Portraits of Centenarians is a photographic book that presents intimate, dignified portraits and life stories of people aged 100 and over.

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_69e25d23a5c88190ae3999c70ca15e08 completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f1cae643448190863c44df5f026482 completed April 29, 2026, 9:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f69c0b684819090df2dab1ddcffcd completed May 21, 2026, 8:23 p.m.
NEDg Description generation batch_6a0f6d42f0dc8190a01c02db0e089d68 completed May 21, 2026, 8:38 p.m.
NED2 Entity disambiguation (via description) batch_6a0f6df970a08190b1d3959a39b30233 completed May 21, 2026, 8:41 p.m.
Created at: April 17, 2026, 8:14 p.m.