Triple

T2947082
Position Surface form Disambiguated ID Type / Status
Subject Northern Spain E79528 entity
Predicate hasMajorCity P316 FINISHED
Object Gijón E269560 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: Gijón | Statement: [Northern Spain, hasMajorCity, Gijón]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gijón
Context triple: [Northern Spain, hasMajorCity, Gijón]
  • A. Gijón chosen
    Gijón is a coastal city in northern Spain’s Asturias region, known for its major seaport, maritime heritage, and beaches along the Bay of Biscay.
  • B. Ferrol
    Ferrol is a coastal city and major naval shipbuilding center in the Galicia region of northwestern Spain.
  • C. Pontevedra
    Pontevedra is a coastal municipality in the province of Capiz in the Philippines, known for its fishing communities and agricultural economy.
  • D. Pontevedra
    Pontevedra is a coastal province in northwestern Spain known for its historic towns, Atlantic landscapes, and location within the autonomous community of Galicia.
  • E. A Coruña
    A Coruña is a coastal city in northwestern Spain known for its historic lighthouse, the Tower of Hercules, and its role as an important cultural and economic center in the region.
  • 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_69ad8b1089588190b74d9e2505e45762 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad98b5916c8190b1163bf0b7fa136a completed March 8, 2026, 3:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69b08695bea08190abce552493abda57 completed March 10, 2026, 9:01 p.m.
Created at: March 8, 2026, 2:57 p.m.