Triple

T5701869
Position Surface form Disambiguated ID Type / Status
Subject ZLP E125682 entity
Predicate relatedCode P5026 FINISHED
Object ZRH E70621 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: ZRH | Statement: [ZLP, relatedCode, ZRH]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ZRH
Context triple: [ZLP, relatedCode, ZRH]
  • A. Zurich Airport chosen
    Zurich Airport is Switzerland’s largest and busiest international airport, serving as a major European aviation hub near the city of Zurich.
  • B. Zurich
    Zurich is the largest city in Switzerland, known as a global financial hub and cultural center situated on the shores of Lake Zurich.
  • C. Zurich Airport International
    Zurich Airport International is a global airport development and operations company best known for operating Switzerland’s Zurich Airport and managing international airport projects through public–private partnerships.
  • D. EuroAirport Basel–Mulhouse–Freiburg
    EuroAirport Basel–Mulhouse–Freiburg is a unique tri-national international airport jointly operated by France and Switzerland that serves the Basel, Mulhouse, and Freiburg region.
  • E. Zug
    Zug is a small, affluent Swiss city and canton known for its low taxes, picturesque lakeside setting, and role as a hub for international businesses and cryptocurrency companies.
  • 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_69c0082c96988190b3a6a201edce472a completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c0245581988190a819b8137533ed31 completed March 22, 2026, 5:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c05a5fe4fc8190944a63a29da0fe3c completed March 22, 2026, 9:08 p.m.
Created at: March 22, 2026, 3:45 p.m.