Triple

T6474955
Position Surface form Disambiguated ID Type / Status
Subject Southern Luzon E146048 entity
Predicate includesProvince P11085 FINISHED
Object Laguna E190593 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: Laguna | Statement: [Southern Luzon, includesProvince, Laguna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Laguna
Context triple: [Southern Luzon, includesProvince, Laguna]
  • A. Laguna chosen
    Laguna is a province in the Philippines known for its hot springs, lakeside towns around Laguna de Bay, and as the birthplace of national hero José Rizal.
  • B. Laguna
    Laguna is the internal codename Apple used for its early Macintosh Portable computer model.
  • C. Laguna San Rafael
    Laguna San Rafael is a glacial lagoon in southern Chile famed for its dramatic icebergs and proximity to the San Rafael Glacier within Laguna San Rafael National Park.
  • D. Laguna Miscanti
    Laguna Miscanti is a high-altitude Andean lake in northern Chile famed for its deep blue waters, surrounding volcanoes, and striking desert landscape.
  • E. Laguna del Condado
    Laguna del Condado is a coastal lagoon in the Condado district of San Juan, Puerto Rico, known for its calm waters, urban shoreline, and recreational activities like kayaking and paddleboarding.
  • 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_69c653a595b881909e5d3cb781ad5ad4 completed March 27, 2026, 9:53 a.m.
Created at: March 22, 2026, 4:50 p.m.