Triple

T6816495
Position Surface form Disambiguated ID Type / Status
Subject Barbara Siggers Franklin E156772 entity
Predicate familyName P18 FINISHED
Object Franklin E58775 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: Franklin | Statement: [Barbara Siggers Franklin, familyName, Franklin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Franklin
Context triple: [Barbara Siggers Franklin, familyName, Franklin]
  • A. Franklin
    Franklin is the given name of Franklin D. Roosevelt, the 32nd president of the United States who led the country through the Great Depression and World War II.
  • B. Franklin chosen
    Franklin is a common English surname borne by numerous notable individuals across fields such as politics, science, and the arts.
  • C. Franklin
    Franklin is a thoughtful and good-natured friend of Charlie Brown in the Peanuts comic strip, notable as one of the first Black characters in mainstream American comics.
  • D. Franklin
    Franklin McCain was an American civil rights activist best known as one of the Greensboro Four who led the 1960 Woolworth’s lunch counter sit-in in North Carolina.
  • E. Franklin
    Franklin is an independent city in southeastern Virginia known for its small-town character and historical ties to the regional lumber and paper industries.
  • 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_69c68828b26c819090fe9df7612bbc27 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d32dc19c8190a871cc1ff1471a58 completed March 27, 2026, 6:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69c723e0c62c8190b3b3b092ea48d4c5 completed March 28, 2026, 12:42 a.m.
Created at: March 27, 2026, 2:17 p.m.