Triple

T5096342
Position Surface form Disambiguated ID Type / Status
Subject Tacloban E114874 entity
Predicate locatedIn P40 FINISHED
Object Leyte E32776 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: Leyte | Statement: [Tacloban, locatedIn, Leyte]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Leyte
Context triple: [Tacloban, locatedIn, Leyte]
  • A. Leyte chosen
    Leyte is a large island province in the Eastern Visayas region of the Philippines, known for its rich cultural traditions and historical significance, including major World War II events.
  • B. Samar Province
    Samar Province is a largely rural island province in the Eastern Visayas region of the Philippines, known for its rugged landscapes, caves, and strong Waray-speaking cultural heritage.
  • C. Guimaras
    Guimaras is a small island province in the Philippines known for its mango production, coastal scenery, and predominantly Hiligaynon-speaking population.
  • D. Bohol Island
    Bohol Island is a popular island province in the central Philippines known for its Chocolate Hills, tarsier sanctuaries, and white-sand beaches.
  • E. Palawan
    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.
  • 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_69bd443fc49c819089629c00e311310c completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd75652a8081908386718f1fdb1de3 completed March 20, 2026, 4:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf06a25110819080a4cbd13555e652 completed March 21, 2026, 8:59 p.m.
Created at: March 20, 2026, 1:40 p.m.