Triple

T21601823
Position Surface form Disambiguated ID Type / Status
Subject Marlene Tromp E533060 entity
Predicate givenName P17 FINISHED
Object Marlene NE NERFINISHED

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: Marlene | Statement: [Marlene Tromp, givenName, Marlene]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marlene
Context triple: [Marlene Tromp, givenName, Marlene]
  • A. Marlene
    Marlene is an energetic and friendly otter who appears as a main supporting character in the animated series "The Penguins of Madagascar."
  • B. Marlene
    Marlene is the ambitious, career-driven protagonist of Caryl Churchill’s play "Top Girls," whose life embodies the tensions between feminism, success, and personal sacrifice.
  • C. Marlene chosen
    Marlene is a feminine given name of German origin, often considered a blend of Maria and Magdalene and associated with figures like actress Marlene Dietrich.
  • D. Marlene
    Marlene is the determined and pragmatic leader of the Fireflies resistance group in The Last of Us universe.
  • E. Marlene
    Marlene is one of the assumed identities of the traumatized young woman at the center of the psychological drama film "Martha Marcy May Marlene."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c46364608190a337dc8720dc2a35 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef17e247588190a57f27ba04f4f21a completed April 27, 2026, 8:01 a.m.
Created at: April 16, 2026, 6:33 p.m.