Triple

T4380120
Position Surface form Disambiguated ID Type / Status
Subject Port of Batangas E99107 entity
Predicate connectsTo P845 FINISHED
Object Romblon E192440 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: Romblon | Statement: [Port of Batangas, connectsTo, Romblon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Romblon
Context triple: [Port of Batangas, connectsTo, Romblon]
  • A. Romblon chosen
    Romblon is an island province in the Philippines known for its marble industry, clear waters, and scenic beaches.
  • B. 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.
  • C. Marinduque
    Marinduque is an island province in the Philippines known for its heart-shaped geography and the annual Moriones Festival.
  • D. Siquijor
    Siquijor is a small island province in the central Philippines known for its white-sand beaches, coral reefs, and folklore surrounding mysticism and traditional healing.
  • E. Oriental Mindoro
    Oriental Mindoro is a province in the Mimaropa region of the Philippines known for its agricultural economy, coastal communities, and popular tourist destinations like Puerto Galera.
  • 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_69b3454ea8f48190a49c2436624d6ef6 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3524154dc81908532cdf997dcb802 completed March 12, 2026, 11:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b672218c1c8190adbb3c044b648e7e completed March 15, 2026, 8:47 a.m.
Created at: March 12, 2026, 11:18 p.m.