Triple

T11440974
Position Surface form Disambiguated ID Type / Status
Subject Zaragoza Airport E271141 entity
Predicate serves P98 FINISHED
Object city of Zaragoza E55920 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: city of Zaragoza | Statement: [Zaragoza Airport, serves, city of Zaragoza]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: city of Zaragoza
Context triple: [Zaragoza Airport, serves, city of Zaragoza]
  • A. Zaragoza
    Zaragoza is a metro station on Mexico City’s Line 1 that serves as a key eastern access point to the city’s rapid transit network.
  • B. Zaragoza chosen
    Zaragoza is a historic city in northeastern Spain, known for landmarks like the Basilica del Pilar and its role as a major cultural and economic center in the Aragon region.
  • C. Zaragoza
    Zaragoza is a small municipality and town in the northern Mexican state of Coahuila, known for its rural character and proximity to the U.S. border.
  • D. Zaragosa
    Zaragosa is a barangay (village-level administrative division) within the municipality of Badian in the province of Cebu, Philippines.
  • E. Zaragoza (town)
    Zaragoza (town) is a small municipality in the Mexican state of Puebla, known for its local agricultural economy and role as an administrative center for the surrounding rural area.
  • 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_69d6aadeef688190874bcecd88b3dd9b completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d80888190c8190b6365550ffe4931c completed April 9, 2026, 8:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69e624782cf88190b696a1c7c9a56395 completed April 20, 2026, 1:04 p.m.
Created at: April 8, 2026, 9:35 p.m.