Triple

T598672
Position Surface form Disambiguated ID Type / Status
Subject Robert Rogers E11443 entity
Predicate parent P120 FINISHED
Object James Rogers E36090 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: James Rogers | Statement: [Robert Rogers, parent, James Rogers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: James Rogers
Context triple: [Robert Rogers, parent, James Rogers]
  • A. James Rogers chosen
    James Rogers is a common personal name shared by numerous individuals across various fields, including politics, academia, sports, and the arts.
  • B. Michael Rogers
    Michael Rogers is a relatively common personal name shared by multiple individuals across fields such as politics, sports, and the arts, rather than referring to one singular widely recognized figure.
  • C. David Drumlin
    David Drumlin is a high-ranking government science advisor and political figure in the science fiction film "Contact," often serving as a skeptical foil to the protagonist Ellie Arroway.
  • D. James Honaker
    James Honaker is a political scientist and statistician known for his work on methods for handling missing data and for coauthoring influential research with Gary King.
  • E. Ted Cheesman
    Ted Cheesman was a film editor best known for his work on classic Hollywood productions, including the 1933 monster film "King Kong."
  • 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_69a4932779b881908688590d59c71900 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49d776c6c819081b41a9b55041cd5 completed March 1, 2026, 8:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac4279b23c8190854732f4d6d5d6cd completed March 7, 2026, 3:21 p.m.
Created at: March 1, 2026, 7:35 p.m.