Triple

T3503832
Position Surface form Disambiguated ID Type / Status
Subject Cavite E74028 entity
Predicate locatedIn P40 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: [Cavite, locatedIn, Calabarzon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Calabarzon
Context triple: [Cavite, locatedIn, 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. Malabuyoc
    Malabuyoc is a coastal municipality in the southwestern part of Cebu province in the Philippines, known for its hot springs and scenic seaside landscapes.
  • 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbbf0c2b48190b49923137bb9e45d completed March 8, 2026, 6:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69b373db933881908557d678dcdee382 completed March 13, 2026, 2:18 a.m.
Created at: March 8, 2026, 3:18 p.m.