Triple

T13150678
Position Surface form Disambiguated ID Type / Status
Subject San Fernando Region E312455 entity
Predicate hasName P744 FINISHED
Object San Fernando Region E312455 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: San Fernando Region | Statement: [San Fernando Region, hasName, San Fernando Region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Fernando Region
Context triple: [San Fernando Region, hasName, San Fernando Region]
  • A. San Fernando Region chosen
    San Fernando Region is an administrative region that encompasses the city of San Fernando and its surrounding areas.
  • B. Lagunas region
    The Lagunas region is a subregion of the Mexican state of Jalisco known for its numerous lakes and lagoons, which shape its local economy and landscape.
  • C. Laguna Region
    The Laguna Region is an important industrial and agricultural area in northern Mexico, centered around the cities of Torreón, Gómez Palacio, and Lerdo, known especially for cotton production and dairy farming.
  • D. Norte Samareño
    Norte Samareño is the term used to refer to residents or natives of the Philippine province of Northern Samar.
  • E. Soccsksargen region
    Soccsksargen is an administrative region in south-central Mindanao in the Philippines, known for its rich fisheries, agriculture, and diverse cultural communities.
  • 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_69d806aabde48190899e13e41659cae5 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98bd1fc408190b4b5ca973bcee403 completed April 10, 2026, 11:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6eaea3f888190b47ed7bf1c52e7e7 completed May 3, 2026, 6:27 a.m.
Created at: April 9, 2026, 9:11 p.m.