Triple

T3269825
Position Surface form Disambiguated ID Type / Status
Subject Guanajuato E68616 entity
Predicate hasCity P316 FINISHED
Object Silao E63068 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: Silao | Statement: [Guanajuato, hasCity, Silao]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Silao
Context triple: [Guanajuato, hasCity, Silao]
  • A. Surigaonon
    Surigaonon is a Visayan language spoken primarily in the Caraga region of northeastern Mindanao in the Philippines.
  • B. Malpaso
    Malpaso is the highest peak on the Canary Island of El Hierro, known for its panoramic views over the island and surrounding Atlantic Ocean.
  • C. Canlaon
    Canlaon is a city in the Philippines known for its proximity to Mount Kanlaon, an active volcano and prominent natural landmark on Negros Island.
  • D. Silao Municipality chosen
    Silao Municipality is an administrative region in the state of Guanajuato, Mexico, known for its industrial activity and proximity to the city of León and the Bajío industrial corridor.
  • E. Los Baños
    Los Baños is a municipality in the Philippines known as a major center for agricultural research and education, particularly in rice science.
  • 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_69ad859b54f881909bf530d549caf2fd completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adaff349148190beae8c0994b7ad83 completed March 8, 2026, 5:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69b28efded588190bd6c361e5298b496 completed March 12, 2026, 10:01 a.m.
Created at: March 8, 2026, 3:09 p.m.