Triple

T7606630
Position Surface form Disambiguated ID Type / Status
Subject Megan Hipwell E180120 entity
Predicate relationshipTo P37 FINISHED
Object Rachel Watson E220703 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: Rachel Watson | Statement: [Megan Hipwell, relationshipTo, Rachel Watson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rachel Watson
Context triple: [Megan Hipwell, relationshipTo, Rachel Watson]
  • A. Rachel Watson chosen
    Rachel Watson is the troubled, alcoholic protagonist of "The Girl on the Train," whose obsession with her former life and the people she observes from her daily commute entangles her in a missing-person investigation.
  • B. Emily Ruth Watson
    Emily Ruth Watson is known as the wife of the late American soul musician and actor Isaac Hayes.
  • C. Sheila Watson
    Sheila Watson was a pioneering Canadian modernist writer and critic best known for her influential novel "The Double Hook," which helped shape the development of contemporary Canadian literature.
  • D. Rachel Matthews
    Rachel Matthews is an American actress best known for her voice role in Disney's animated film Frozen II.
  • E. Jessica Fitzwater
    Jessica Fitzwater is an American politician who serves as the chief executive of Frederick County, Maryland, overseeing the county’s government and administration.
  • 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_69c69f3567008190ab01d2ca7b53584a completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c6f9fe10408190b1c12bb8f911cea8 completed March 27, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69c87094139481909858399a082f4a29 completed March 29, 2026, 12:21 a.m.
Created at: March 27, 2026, 3:54 p.m.