Triple

T4289689
Position Surface form Disambiguated ID Type / Status
Subject Kate Beckinsale E97356 entity
Predicate workedWith P398 FINISHED
Object Ben Affleck E40496 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: Ben Affleck | Statement: [Kate Beckinsale, workedWith, Ben Affleck]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ben Affleck
Context triple: [Kate Beckinsale, workedWith, Ben Affleck]
  • A. Ben Affleck chosen
    Ben Affleck is an American actor, director, and screenwriter known for films such as "Good Will Hunting," "Argo," and for portraying Batman in the DC Extended Universe.
  • B. Matt Damon
    Matt Damon is an American actor, producer, and screenwriter known for his versatile performances in films such as Good Will Hunting, the Bourne series, and The Martian.
  • C. Affleck
    Affleck is a surname most prominently associated with American actor and filmmaker Ben Affleck and his brother, actor Casey Affleck.
  • D. Afflecks
    Afflecks is an iconic indoor market and alternative shopping emporium in Manchester known for its independent retailers, vintage fashion, and vibrant subcultural atmosphere.
  • E. Edward Norton
    Edward Norton is an acclaimed American actor and filmmaker known for his intense, nuanced performances in films such as "Fight Club," "American History X," and "Birdman."
  • 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_69b3454595848190a0e6bbb6a2bea040 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b35061f5448190b3356b29a9129160 completed March 12, 2026, 11:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5c7307e3481909dfb55018f359589 completed March 14, 2026, 8:38 p.m.
Created at: March 12, 2026, 11:08 p.m.