Triple

T11297669
Position Surface form Disambiguated ID Type / Status
Subject UP Open University E267495 entity
Predicate province P604 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: [UP Open University, province, Laguna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Laguna
Context triple: [UP Open University, province, 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. Lagunas
    Lagunas is a municipality and town in the state of Jalisco, Mexico, known for its rural character and proximity to the Sierra de Amula region.
  • D. 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.
  • E. 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.
  • 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_69d6aac993a08190a6f36445ebaf9a43 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e9a3616c8190a8fd23ca67463806 completed April 9, 2026, 6:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69e50a3e26e88190991127a5993a32a4 completed April 19, 2026, 5 p.m.
Created at: April 8, 2026, 9:32 p.m.