Triple

T7696028
Position Surface form Disambiguated ID Type / Status
Subject Rizal E174371 entity
Predicate hasMunicipality P847 FINISHED
Object Taytay E430651 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: Taytay | Statement: [Rizal, hasMunicipality, Taytay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taytay
Context triple: [Rizal, hasMunicipality, Taytay]
  • A. Taytay chosen
    Taytay is a municipality in the province of Rizal in the Philippines, known as a rapidly urbanizing suburban area just east of Metro Manila.
  • B. Santanyí
    Santanyí is a picturesque coastal town in southeastern Mallorca, Spain, known for its traditional stone architecture, weekly markets, and nearby sandy coves.
  • C. Cullera
    Cullera is a coastal town in eastern Spain known for its Mediterranean beaches, historic castle, and location at the mouth of the Júcar River.
  • D. Sotres
    Sotres is a small mountain village in Asturias, northern Spain, known as a popular base for hiking and accessing the Picos de Europa.
  • E. Garraf
    Garraf is a coastal comarca in Catalonia, Spain, known for its Mediterranean landscapes, natural park, and seaside towns such as Sitges and Vilanova i la Geltrú.
  • 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_69c6995966348190939e6c37ba272c06 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c70267dab88190ac8e3f643343bf13 completed March 27, 2026, 10:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8b50049f88190b4cd5cf692d0a3b1 completed March 29, 2026, 5:13 a.m.
Created at: March 27, 2026, 4:03 p.m.