Triple

T3069630
Position Surface form Disambiguated ID Type / Status
Subject Sulu Sea E62188 entity
Predicate borderedBy P224 FINISHED
Object Palawan E190594 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: Palawan | Statement: [Sulu Sea, borderedBy, Palawan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Palawan
Context triple: [Sulu Sea, borderedBy, Palawan]
  • A. Palawan chosen
    Palawan is a large island province in the western Philippines known for its stunning limestone cliffs, clear turquoise waters, rich marine biodiversity, and popular ecotourism destinations like El Nido and Puerto Princesa.
  • B. Yap State
    Yap State is one of the four constituent states of the Federated States of Micronesia, known for its traditional stone money and rich Micronesian cultural heritage.
  • C. Romblon
    Romblon is an island province in the Philippines known for its marble industry, clear waters, and scenic beaches.
  • D. Dinagat Islands
    Dinagat Islands is a province in the Caraga region of the Philippines known for its rugged coastline, rich marine biodiversity, and relatively remote, less-developed island communities.
  • E. Capiz
    Capiz is a province in the Western Visayas region of the Philippines, known for its coastal landscapes, seafood, and use of the Hiligaynon language.
  • 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_69ad85793e5c8190a358049bc4a98d8c completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada100f0b8819095da366fdc6803a8 completed March 8, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69b31a4da4b48190987aa1c1f5f61fd9 completed March 12, 2026, 7:55 p.m.
Created at: March 8, 2026, 3:02 p.m.