Triple

T10380089
Position Surface form Disambiguated ID Type / Status
Subject Marie Kreutz E244614 entity
Predicate settingOfKeyScenes P1957 FINISHED
Object Zurich E13407 NE FINISHED

How this triple was built (3 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: Zurich | Statement: [Marie Kreutz, settingOfKeyScenes, Zurich]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zurich
Context triple: [Marie Kreutz, settingOfKeyScenes, Zurich]
  • A. Zurich chosen
    Zurich is the largest city in Switzerland, known as a global financial hub and cultural center situated on the shores of Lake Zurich.
  • B. Stettlen
    Stettlen is a municipality in the canton of Bern in Switzerland, situated just east of the city of Bern and functioning largely as a residential and commuter community.
  • C. Berne
    Berne is the de facto capital city of Switzerland and the seat of its federal government institutions.
  • D. Geneva
    Geneva is a major Swiss city on Lake Geneva known for hosting numerous international organizations, including United Nations agencies and the Red Cross.
  • E. Geneva
    Geneva is a small city in northeastern Ohio situated along Lake Erie, known for its wineries, tourism, and location within the Rust Belt region.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: settingOfKeyScenes
Context triple: [Marie Kreutz, settingOfKeyScenes, Zurich]
  • A. filmSceneType
    Indicates the type or category of a scene within a film, such as its narrative function, style, or setting.
  • B. notableScene
    Indicates that a particular scene is especially significant, memorable, or noteworthy within a work or context.
  • C. performedInSceneType
    Indicates that an action or event was carried out within a scene of a specified type or category.
  • D. partOfScene
    Indicates that one entity functions as a component or element within a larger scene or setting involving another entity.
  • E. setting chosen
    Indicates the place, time, or context in which an event, action, or interaction occurs.
  • F. None of above.

Provenance (4 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_69d381b3e328819094b23b8edcd29b5a completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e991056c8190a981f717c51f1f72 completed April 7, 2026, 11:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69dbb6cbdd30819087c3d980ab68c44e completed April 12, 2026, 3:14 p.m.
PD Predicate disambiguation batch_69d4dfb0e7a88190bec0b7a52c70dfe2 completed April 7, 2026, 10:42 a.m.
Created at: April 6, 2026, 12:03 p.m.