Triple

T14657124
Position Surface form Disambiguated ID Type / Status
Subject Clean and Sober E344139 entity
Predicate cinematography P1953 FINISHED
Object Michael Ballhaus E364560 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: Michael Ballhaus | Statement: [Clean and Sober, cinematography, Michael Ballhaus]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Ballhaus
Context triple: [Clean and Sober, cinematography, Michael Ballhaus]
  • A. Michael Ballhaus chosen
    Michael Ballhaus was a renowned German cinematographer celebrated for his dynamic camera work and frequent collaborations with director Martin Scorsese.
  • B. Paul Weinert
    Paul Weinert was a United States Army soldier and Medal of Honor recipient recognized for his bravery during the Indian Wars.
  • C. Paul Zimmerer
    Paul Zimmerer was an American entrepreneur best known as the founder of Lindsay Corporation, a major manufacturer of agricultural irrigation and infrastructure equipment.
  • D. Paul Biegler
    Paul Biegler is a small-town Michigan lawyer and the central protagonist of the courtroom drama novel and film "Anatomy of a Murder."
  • E. David Hohnen
    David Hohnen is a prominent New Zealand-based winemaker and wine industry pioneer best known for co-founding Cloudy Bay and helping popularize Marlborough Sauvignon Blanc globally.
  • 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_69d822e1a2cc81908e5bb93cf61ce3cc completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb51a562c819098971447db4b29f7 completed April 14, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe24a6b0908190943f193f5a17ec8c completed May 8, 2026, 6 p.m.
Created at: April 10, 2026, 1:27 a.m.