Triple

T20041285
Position Surface form Disambiguated ID Type / Status
Subject Liria E497424 entity
Predicate hasNameInSpanish P12773 FINISHED
Object Liria NE NERFINISHED

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: Liria | Statement: [Liria, hasNameInSpanish, Liria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Liria
Context triple: [Liria, hasNameInSpanish, Liria]
  • A. Liria chosen
    Liria is a historic town in the Valencian Community of Spain, known for its ancient Iberian and Roman heritage and its role as a noble title’s namesake.
  • B. Fuenla
    Fuenla is the popular nickname of CF Fuenlabrada, a Spanish football club based in the city of Fuenlabrada in the Community of Madrid.
  • C. Merania
    Merania was a medieval duchy on the Adriatic coast, historically associated with the House of Andechs and various European noble lineages.
  • D. Alcaës
    Alcaës is a figure from Greek mythology, likely a minor or variant character associated with the mythological traditions surrounding Lysidice.
  • E. Laviana
    Laviana is a municipality in the Asturias region of northern Spain, situated in the Nalón River valley and known for its mining heritage and mountainous surroundings.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da627278c88190babe4297a9df1236 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e662eb9a6081909d06dc1d457b4d5a completed April 20, 2026, 5:31 p.m.
Created at: April 11, 2026, 3:37 p.m.