Triple

T1430809
Position Surface form Disambiguated ID Type / Status
Subject Pablo E30439 entity
Predicate hasEquivalentNameInCatalan P28329 FINISHED
Object Pau E49264 NE FINISHED

How this triple was built (3 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: Pau | Statement: [Pablo, hasEquivalentNameInCatalan, Pau]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pau
Context triple: [Pablo, hasEquivalentNameInCatalan, Pau]
  • A. Pau chosen
    Pau is a historic city in southwestern France, known as the capital of the Pyrénées-Atlantiques department and for its scenic location near the Pyrenees mountains.
  • B. Pau-Ferro
    Pau-Ferro is a neighborhood in the city of Recife, Brazil.
  • C. Toulouse
    Toulouse is a major city in southwestern France known for its aerospace industry, historic pink-brick architecture, and vibrant university and cultural life.
  • D. Blagnac
    Blagnac is a suburban town near Toulouse in southwestern France, best known for hosting the headquarters and main facilities of the aircraft manufacturer Airbus.
  • E. Montauban
    Montauban is a historic city in southern France known for its red-brick architecture and role as the capital of the Tarn-et-Garonne department.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasEquivalentNameInCatalan
Context triple: [Pablo, hasEquivalentNameInCatalan, Pau]
  • A. hasNameInSpanish
    Indicates that an entity is associated with a specific name expressed in the Spanish language.
  • B. hasBasqueName
    Indicates that an entity is associated with a name expressed in the Basque language.
  • C. languageEquivalent chosen
    Indicates that two linguistic expressions convey the same meaning or function across different languages or language varieties.
  • D. hasNameInLocalLanguage
    Indicates that an entity is associated with a name expressed in the local or native language of a given context or region.
  • E. hasEnglishName
    Indicates that an entity is associated with a name expressed in the English language.
  • F. None of above.

Provenance (4 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_69a498fc69ec8190b61722bd4b67c4d2 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c500a9888190a16fbb1ec97a79c9 completed March 1, 2026, 11 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad016bf2608190a675cbd42e474082 completed March 8, 2026, 4:56 a.m.
PD Predicate disambiguation batch_69a4c4771c9481908ae47c959debbe77 completed March 1, 2026, 10:57 p.m.
Created at: March 1, 2026, 8 p.m.