Triple

T5992434
Position Surface form Disambiguated ID Type / Status
Subject Elliott Erwitt E133382 entity
Predicate familyName P18 FINISHED
Object Erwitt
Erwitt is the surname of Elliott Erwitt, a renowned American photographer celebrated for his candid and often humorous black-and-white images.
 E562013 NE FINISHED

How this triple was built (4 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: Erwitt | Statement: [Elliott Erwitt, familyName, Erwitt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Erwitt
Context triple: [Elliott Erwitt, familyName, Erwitt]
  • A. Soest
    Soest is a historic town in North Rhine-Westphalia, Germany, known for its well-preserved medieval architecture and former significance as a Hanseatic trading center.
  • B. Soest
    Soest is a Dutch town and municipality in the central Netherlands known for its green surroundings and proximity to the Utrechtse Heuvelrug.
  • C. Othmarschen
    Othmarschen is a residential district in the west of Hamburg, Germany, known for its affluent neighborhoods, green spaces, and location along the Elbe River.
  • D. Eiderstedt
    Eiderstedt is a low-lying peninsula on Germany’s North Sea coast known for its dike-protected marshlands, agriculture, and coastal tourism.
  • E. Melchow
    Melchow is a small municipality in the Barnim district of the federal state of Brandenburg in northeastern Germany.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Erwitt
Triple: [Elliott Erwitt, familyName, Erwitt]
Generated description
Erwitt is the surname of Elliott Erwitt, a renowned American photographer celebrated for his candid and often humorous black-and-white images.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Erwitt
Target entity description: Erwitt is the surname of Elliott Erwitt, a renowned American photographer celebrated for his candid and often humorous black-and-white images.
  • A. Soest
    Soest is a historic town in North Rhine-Westphalia, Germany, known for its well-preserved medieval architecture and former significance as a Hanseatic trading center.
  • B. Soest
    Soest is a Dutch town and municipality in the central Netherlands known for its green surroundings and proximity to the Utrechtse Heuvelrug.
  • C. Othmarschen
    Othmarschen is a residential district in the west of Hamburg, Germany, known for its affluent neighborhoods, green spaces, and location along the Elbe River.
  • D. Eiderstedt
    Eiderstedt is a low-lying peninsula on Germany’s North Sea coast known for its dike-protected marshlands, agriculture, and coastal tourism.
  • E. Melchow
    Melchow is a small municipality in the Barnim district of the federal state of Brandenburg in northeastern Germany.
  • F. None of above. chosen

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_69c00870ddbc81909880fa3864f4f38d completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c04e9189908190abc9c742b38dfabc completed March 22, 2026, 8:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c108685f788190aa3f84e56837b195 completed March 23, 2026, 9:31 a.m.
NEDg Description generation batch_69c10a15b9e08190be8b559e467d1d8c completed March 23, 2026, 9:38 a.m.
NED2 Entity disambiguation (via description) batch_69c10aaf299481909d0e824a126381e3 completed March 23, 2026, 9:41 a.m.
Created at: March 22, 2026, 4:05 p.m.