Triple

T6475024
Position Surface form Disambiguated ID Type / Status
Subject Southern Luzon E146048 entity
Predicate hasIsland P970 FINISHED
Object Marinduque E188574 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: Marinduque | Statement: [Southern Luzon, hasIsland, Marinduque]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marinduque
Context triple: [Southern Luzon, hasIsland, Marinduque]
  • A. Marinduque chosen
    Marinduque is an island province in the Philippines known for its heart-shaped geography and the annual Moriones Festival.
  • B. 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.
  • C. Occidental Mindoro
    Occidental Mindoro is a province in the Mimaropa region of the Philippines known for its agricultural economy, coastal communities, and proximity to the island of Mindoro’s rich marine and natural resources.
  • D. Catanduanes
    Catanduanes is an island province in the Bicol Region of the Philippines known for its rugged coastlines, surfing beaches, and predominantly Bikol-speaking population.
  • E. Romblon
    Romblon is an island province in the Philippines known for its marble industry, clear waters, and scenic beaches.
  • 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_69c008fec7408190af7b146dc63d9750 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c06a341360819082f2b5496a1a68b0 completed March 22, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c70adfd6e48190badc31135f9b69a3 completed March 27, 2026, 10:55 p.m.
Created at: March 22, 2026, 4:50 p.m.