Triple

T16037033
Position Surface form Disambiguated ID Type / Status
Subject Ala 15 E388994 entity
Predicate basedIn P40 FINISHED
Object Zaragoza, Spain 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: Zaragoza, Spain | Statement: [Ala 15, basedIn, Zaragoza, Spain]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zaragoza, Spain
Context triple: [Ala 15, basedIn, Zaragoza, Spain]
  • 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 barangay (village-level administrative division) within the municipality of Oton in the province of Iloilo, Philippines.
  • D. 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.
  • E. Alburquerque, Spain
    Alburquerque, Spain is a historic town in the Extremadura region near the Portuguese border, known for its medieval castle and strategic frontier location.
  • 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_69d86dada3808190825d5f80d72fbe88 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1833da68881908710fb2c28e8c6d0 completed April 17, 2026, 12:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69fff295a0e08190b80d363f0a48094a completed May 10, 2026, 2:51 a.m.
Created at: April 10, 2026, 4:56 a.m.