Triple

T10798315
Position Surface form Disambiguated ID Type / Status
Subject Harlow Shapley E254768 entity
Predicate givenName P17 FINISHED
Object Harlow E163496 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: Harlow | Statement: [Harlow Shapley, givenName, Harlow]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Harlow
Context triple: [Harlow Shapley, givenName, Harlow]
  • A. Harlow
    Harlow is a 1965 biographical drama film about the life and career of Hollywood actress Jean Harlow.
  • B. Harlow chosen
    Harlow is a town in Essex, England, known as a post-war New Town with significant residential, commercial, and industrial development.
  • C. Peabody
    Peabody is a suburban city in northeastern Massachusetts known for its location on the North Shore and its historical ties to the leather industry.
  • D. Haddon Heights
    Haddon Heights is a small suburban borough in southern New Jersey known for its historic homes, tree-lined streets, and close-knit community.
  • E. Marford
    Marford is a village in Wrexham County Borough, Wales, known for its distinctive Gothic-style architecture and historic character.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6aa61c15c8190a1839550c56e75e1 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d73334feb08190aae967eaa37659f7 completed April 9, 2026, 5:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69de566352608190ab15e3a4b690c9a5 completed April 14, 2026, 2:59 p.m.
Created at: April 8, 2026, 9:17 p.m.