Triple

T8161789
Position Surface form Disambiguated ID Type / Status
Subject Greater Luzon E190592 entity
Predicate hasPart P35 FINISHED
Object Calabarzon E97442 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: Calabarzon | Statement: [Greater Luzon, hasPart, Calabarzon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Calabarzon
Context triple: [Greater Luzon, hasPart, Calabarzon]
  • A. Calabarzon chosen
    Calabarzon is a populous and industrialized region in the southern part of Luzon in the Philippines, known for its mix of urban centers, agricultural areas, and manufacturing hubs.
  • B. Aguiguan
    Aguiguan is a small, uninhabited island in the Northern Mariana Islands known for its rugged terrain and seabird colonies.
  • C. Ibanag
    Ibanag is an Austronesian language spoken primarily in the Cagayan Valley region of northern Luzon in the Philippines.
  • D. Sarangani
    Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
  • E. Gumaca
    Gumaca is a coastal municipality in the province of Quezon in the Philippines, known for its historic churches and role as a local commercial center.
  • 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_69ca82c0ef14819083713f4473dd847c completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb4556b45c819089eb15ad027b036a completed March 31, 2026, 3:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd67a886948190a728909f8381c2f1 completed April 1, 2026, 6:44 p.m.
Created at: March 30, 2026, 5:38 p.m.