Triple

T10551501
Position Surface form Disambiguated ID Type / Status
Subject Todmorden Town Hall E248957 entity
Predicate architect P184 FINISHED
Object John Gibson
John Gibson was a 19th-century British architect known for designing prominent public buildings in a classical style.
E876897 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: John Gibson | Statement: [Todmorden Town Hall, architect, John Gibson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Gibson
Context triple: [Todmorden Town Hall, architect, John Gibson]
  • A. John Gibson
    John Gibson is an American professional ice hockey goaltender best known for his standout NHL career with the Anaheim Ducks and international play for Team USA.
  • B. Michel Gibson
    Michel Gibson is a local political figure who serves as the mayor of Kirkland, overseeing the city's municipal government and public affairs.
  • C. Daniel Gibson
    Daniel Gibson is a former American professional basketball player who played as a guard for the Cleveland Cavaliers in the NBA.
  • D. Robert Gaskins
    Robert Gaskins is a software entrepreneur best known as the co-creator of Microsoft PowerPoint and a key figure in the early development of presentation software.
  • E. Glenn Williamson
    Glenn Williamson is a film producer known for his work on independent and character-driven movies, including the dark comedy-drama "Sunshine Cleaning."
  • 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: John Gibson
Triple: [Todmorden Town Hall, architect, John Gibson]
Generated description
John Gibson was a 19th-century British architect known for designing prominent public buildings in a classical style.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: John Gibson
Target entity description: John Gibson was a 19th-century British architect known for designing prominent public buildings in a classical style.
  • A. John Gibson
    John Gibson is an American professional ice hockey goaltender best known for his standout NHL career with the Anaheim Ducks and international play for Team USA.
  • B. Michel Gibson
    Michel Gibson is a local political figure who serves as the mayor of Kirkland, overseeing the city's municipal government and public affairs.
  • C. Daniel Gibson
    Daniel Gibson is a former American professional basketball player who played as a guard for the Cleveland Cavaliers in the NBA.
  • D. Robert Gaskins
    Robert Gaskins is a software entrepreneur best known as the co-creator of Microsoft PowerPoint and a key figure in the early development of presentation software.
  • E. Glenn Williamson
    Glenn Williamson is a film producer known for his work on independent and character-driven movies, including the dark comedy-drama "Sunshine Cleaning."
  • 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_69d381c733c08190ab1dd6239f5f34ae completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d526d4c4048190a104d6e088f565b3 completed April 7, 2026, 3:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69d97a10f17081909fd9465cf35685a1 completed April 10, 2026, 10:30 p.m.
NEDg Description generation batch_69d97c7bc87481908d50eb6f294170eb completed April 10, 2026, 10:40 p.m.
NED2 Entity disambiguation (via description) batch_69d97e015b088190a97822675eecaa5a completed April 10, 2026, 10:47 p.m.
Created at: April 6, 2026, 12:34 p.m.