Triple

T12613356
Position Surface form Disambiguated ID Type / Status
Subject Red-Headed Woman E301183 entity
Predicate starring P1507 FINISHED
Object Una Merkel E529557 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: Una Merkel | Statement: [Red-Headed Woman, starring, Una Merkel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Una Merkel
Context triple: [Red-Headed Woman, starring, Una Merkel]
  • A. Una Merkel chosen
    Una Merkel was an American stage and film actress best known for her sharp comic timing and memorable supporting roles in Hollywood films of the 1930s and 1940s.
  • B. Angela Merkel
    Angela Merkel is a German politician who served as Chancellor of Germany from 2005 to 2021 and became one of the most influential leaders in Europe and the world.
  • C. Max Merkel
    Max Merkel was a prominent Austrian football manager known for leading several European clubs to success in the 1960s and 1970s.
  • D. Bettina Wulff
    Bettina Wulff is a German public relations consultant and former First Lady of Germany, known for her marriage to former President Christian Wulff and her subsequent media presence.
  • E. Hannelore Kohl
    Hannelore Kohl was a German translator and the longtime wife of former Chancellor Helmut Kohl, known for her public role during his tenure and her later struggles with a debilitating light allergy.
  • 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_69d7bdeaf49c8190b13800111fa77ea3 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d960c2e5b88190a7cc16002b218d8a completed April 10, 2026, 8:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65ed1044c8190bbe881d32a4bf29e completed May 2, 2026, 8:30 p.m.
Created at: April 9, 2026, 5:12 p.m.