Triple

T4888877
Position Surface form Disambiguated ID Type / Status
Subject Canton of Zürich E109508 entity
Predicate contains P35 FINISHED
Object Dietikon E392055 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: Dietikon | Statement: [Canton of Zürich, contains, Dietikon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dietikon
Context triple: [Canton of Zürich, contains, Dietikon]
  • A. Dietikon chosen
    Dietikon is a town and municipality in the canton of Zurich in Switzerland, known as an important regional center in the Limmat Valley.
  • B. Zurich Wiedikon
    Zurich Wiedikon is a residential and commercial district in the city of Zurich, Switzerland, known for its urban character, good public transport connections, and proximity to the Sihl River.
  • C. Kloten
    Kloten is a town in the canton of Zurich in northern Switzerland, best known as the home of Zurich Airport.
  • D. Grenchen
    Grenchen is a Swiss town in the canton of Solothurn known for its watchmaking industry and location at the foot of the Jura Mountains.
  • E. Adliswil
    Adliswil is a municipality in the canton of Zurich, Switzerland, situated in the Sihl Valley just south of the city of Zurich.
  • 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_69bd440f71348190b99938e59fb7f9a1 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6e06a81881908734dbdc350a2039 completed March 20, 2026, 3:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69be9235e7b8819084fd9eb7c794e0e3 completed March 21, 2026, 12:42 p.m.
Created at: March 20, 2026, 1:28 p.m.