Triple

T15743102
Position Surface form Disambiguated ID Type / Status
Subject UC3M E381647 entity
Predicate hasCampus P116 FINISHED
Object Getafe E92560 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: Getafe | Statement: [UC3M, hasCampus, Getafe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Getafe
Context triple: [UC3M, hasCampus, Getafe]
  • A. Getafe chosen
    Getafe is a city in central Spain that forms part of the Madrid metropolitan area and is known for its industrial base, university campus, and air force history.
  • B. Alcorcón
    Alcorcón is a suburban city in central Spain that forms part of the metropolitan area of Madrid.
  • C. Getafe CF
    Getafe CF is a Spanish professional football club based in the Madrid suburb of Getafe that competes in La Liga.
  • D. Valverde de Leganés
    Valverde de Leganés is a municipality in the autonomous community of Extremadura in western Spain, near the border with Portugal.
  • E. Valmadrid
    Valmadrid is a small municipality in the province of Zaragoza, Aragon, Spain, situated within the semi-arid landscapes characteristic of the Campo de Belchite 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_69d86d9e6b44819085d1f6a969ecb74c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e04fd97d6c8190b2fa6ca422bfe512 completed April 16, 2026, 2:56 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff9094b4008190bb5c65fa2bd0f0b5 completed May 9, 2026, 7:52 p.m.
Created at: April 10, 2026, 4:46 a.m.