Triple

T9315837
Position Surface form Disambiguated ID Type / Status
Subject Werner Stengel E224115 entity
Predicate hasCollaboratedWith P8554 FINISHED
Object Zierer E25304 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: Zierer | Statement: [Werner Stengel, hasCollaboratedWith, Zierer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zierer
Context triple: [Werner Stengel, hasCollaboratedWith, Zierer]
  • A. Zierer chosen
    Zierer is a German amusement ride manufacturer known for producing family-friendly roller coasters and classic flat rides for theme parks worldwide.
  • B. Hirzer
    Hirzer is a prominent mountain peak in the Sarntal Alps of South Tyrol, Italy, known for its panoramic hiking routes and scenic alpine views.
  • C. Zurer
    Zurer is the surname of Ayelet Zurer, an Israeli actress known for her roles in international films and television series.
  • D. Kritzinger
    Kritzinger is a German surname most notably associated with Friedrich Wilhelm Kritzinger, a high-ranking Nazi official involved in the administrative planning of the Holocaust.
  • E. Mieresch
    Mieresch is the German name for the Mureș River, a major river flowing through Romania and Hungary.
  • 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_69ca8425f4fc81909c1c586e9a5b7530 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd358846e48190a8aacfab19d88ae7 completed April 1, 2026, 3:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69d0c7acba54819086da668f234321de completed April 4, 2026, 8:11 a.m.
Created at: March 30, 2026, 7:37 p.m.