Triple

T2727487
Position Surface form Disambiguated ID Type / Status
Subject Simón de Anda y Salazar E60227 entity
Predicate placeOfActivity P1527 FINISHED
Object Pampanga E83192 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: Pampanga | Statement: [Simón de Anda y Salazar, placeOfActivity, Pampanga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pampanga
Context triple: [Simón de Anda y Salazar, placeOfActivity, Pampanga]
  • A. Pampanga chosen
    Pampanga is a province in the Central Luzon region of the Philippines, known for its rich culinary heritage, vibrant festivals, and significant role in the country’s history and culture.
  • B. Pangasinan
    Pangasinan is an Austronesian language spoken primarily in the Pangasinan province and surrounding areas of northwestern Luzon in the Philippines.
  • C. Nueva Ecija
    Nueva Ecija is a landlocked agricultural province in Central Luzon, Philippines, known as a major rice-producing area and home to diverse ethnolinguistic groups.
  • D. Bulacan
    Bulacan is a province in the Central Luzon region of the Philippines known for its historical significance, cultural heritage, and proximity to Metro Manila.
  • E. Zambales
    Zambales is a coastal province in the Central Luzon region of the Philippines, known for its beaches, mangoes, and ethnolinguistic diversity.
  • 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_69ab4b75cd908190b691ef0d1801acda completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdacffa6481909df37335e8fdd595 completed March 7, 2026, 7:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69b12dfe893c81909c79ea6cffb5bae6 completed March 11, 2026, 8:55 a.m.
Created at: March 6, 2026, 9:56 p.m.