Triple

T15576936
Position Surface form Disambiguated ID Type / Status
Subject Marc Spector E374392 entity
Predicate loveInterest P7325 FINISHED
Object Marlene Alraune E996173 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: Marlene Alraune | Statement: [Marc Spector, loveInterest, Marlene Alraune]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marlene Alraune
Context triple: [Marc Spector, loveInterest, Marlene Alraune]
  • A. Marlene Alraune chosen
    Marlene Alraune is a key Marvel Comics character closely associated with Moon Knight, often depicted as his love interest and ally in his vigilante activities.
  • B. Lilli Schwarzkopf
    Lilli Schwarzkopf is a German heptathlete who won the silver medal at the 2012 London Olympic Games.
  • C. Rosa Holländer
    Rosa Holländer was a member of the Holländer family and an aunt of diarist Anne Frank through her sister Edith Holländer.
  • D. Marlene Knaus
    Marlene Knaus is an Austrian former model best known as the ex-wife of Formula One legend Niki Lauda.
  • E. Eva Wagner
    Eva Wagner was a daughter of the famed German composer Richard Wagner, belonging to the prominent Wagner family closely associated with the Bayreuth Festival.
  • 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_69d85ccd575081908909b71a3f3e3a61 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04e22c89081909b1ec0cd36a1ef45 completed April 16, 2026, 2:49 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff5f30e48881908b30a71796fe0d72 completed May 9, 2026, 4:22 p.m.
Created at: April 10, 2026, 4:11 a.m.