Triple

T13073450
Position Surface form Disambiguated ID Type / Status
Subject Capul E329510 entity
Predicate hasBarangay P29835 FINISHED
Object San Luis
San Luis is a barangay (village-level administrative division) within the municipality of Capul in the Philippines.
E1023302 NE FINISHED

How this triple was built (4 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 Luis | Statement: [Capul, hasBarangay, San Luis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Luis
Context triple: [Capul, hasBarangay, San Luis]
  • A. San Luis
    San Luis is a municipal barrio (district) of the mountainous town of Aibonito in central Puerto Rico.
  • B. San Luis
    San Luis is a residential and commercial district located in the eastern part of Lima, Peru.
  • C. San Luis
    San Luis is a municipality and town in western Cuba known for its agricultural activities within Pinar del Río Province.
  • D. San Luis
    San Luis is a province in central Argentina known for its mountainous landscapes, arid climate, and role in the country’s early independence era.
  • E. San Luis
    San Luis is a landlocked agricultural municipality in the province of Pampanga in the Philippines, known for its rice fields and rural communities.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: San Luis
Triple: [Capul, hasBarangay, San Luis]
Generated description
San Luis is a barangay (village-level administrative division) within the municipality of Capul in the Philippines.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: San Luis
Target entity description: San Luis is a barangay (village-level administrative division) within the municipality of Capul in the Philippines.
  • A. San Luis
    San Luis is a coastal municipality in the Philippine province of Batangas known for its agricultural economy and small-town rural character.
  • B. San Luis
    San Luis is a landlocked agricultural municipality in the province of Pampanga in the Philippines, known for its rice fields and rural communities.
  • C. San Luis
    San Luis is a municipal barrio (district) of the mountainous town of Aibonito in central Puerto Rico.
  • D. San Luis
    San Luis is a municipality and town in western Cuba known for its agricultural activities within Pinar del Río Province.
  • E. San Luis
    San Luis is a residential and commercial district located in the eastern part of Lima, Peru.
  • F. None of above. chosen

Provenance (5 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_69d80771749c81909a6d9197b9504872 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d981160e388190bab942a2ded2903e completed April 10, 2026, 11 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6e27058dc8190a64e1a929f296619 completed May 3, 2026, 5:51 a.m.
NEDg Description generation batch_69f6e6a8b15081908d80cd63b0c423f6 completed May 3, 2026, 6:09 a.m.
NED2 Entity disambiguation (via description) batch_69f6e7490cc48190b596338cd3a0fd22 completed May 3, 2026, 6:12 a.m.
Created at: April 9, 2026, 9 p.m.