Triple

T2566600
Position Surface form Disambiguated ID Type / Status
Subject Capiz E57364 entity
Predicate hasMunicipality P847 FINISHED
Object Sapian
Sapian is a coastal municipality in the province of Capiz in the Philippines, known for its fishing industry and scenic bay.
 E279155 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: Sapian | Statement: [Capiz, hasMunicipality, Sapian]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sapian
Context triple: [Capiz, hasMunicipality, Sapian]
  • A. Sabaot
    Sabaot is a Southern Nilotic language spoken primarily by the Sabaot people in the Mount Elgon region of Kenya and Uganda.
  • B. Saka
    Saka is an ancient Eastern Iranian language once spoken by the Saka people in the Tarim Basin region of Central Asia.
  • C. Sakaar
    Sakaar is a chaotic, trash-covered planet ruled by the Grandmaster in the Marvel Cinematic Universe, known for its gladiatorial contests and bizarre cosmic detritus.
  • D. Luyana
    Luyana is a Bantu language of southwestern Africa that historically served as a prestige and source language for the development of the Lozi language.
  • E. Saharias
    Saharias are an indigenous tribal community of central India, traditionally known as forest dwellers and laborers with distinct cultural practices and socio-economic challenges.
  • 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: Sapian
Triple: [Capiz, hasMunicipality, Sapian]
Generated description
Sapian is a coastal municipality in the province of Capiz in the Philippines, known for its fishing industry and scenic bay.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sapian
Target entity description: Sapian is a coastal municipality in the province of Capiz in the Philippines, known for its fishing industry and scenic bay.
  • A. Sabaot
    Sabaot is a Southern Nilotic language spoken primarily by the Sabaot people in the Mount Elgon region of Kenya and Uganda.
  • B. Saka
    Saka is an ancient Eastern Iranian language once spoken by the Saka people in the Tarim Basin region of Central Asia.
  • C. Sakaar
    Sakaar is a chaotic, trash-covered planet ruled by the Grandmaster in the Marvel Cinematic Universe, known for its gladiatorial contests and bizarre cosmic detritus.
  • D. Luyana
    Luyana is a Bantu language of southwestern Africa that historically served as a prestige and source language for the development of the Lozi language.
  • E. Saharias
    Saharias are an indigenous tribal community of central India, traditionally known as forest dwellers and laborers with distinct cultural practices and socio-economic challenges.
  • 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_69ab4a4ef9008190a0e6d4422b9418b7 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd3602ed08190aad0f9c7ac577eb0 completed March 7, 2026, 7:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69af6565e05081909dc12aa3240de5f2 completed March 10, 2026, 12:27 a.m.
NEDg Description generation batch_69af667c6b008190b3960f29f5e07653 completed March 10, 2026, 12:31 a.m.
NED2 Entity disambiguation (via description) batch_69af6740cd2c8190a76309238340bd22 completed March 10, 2026, 12:35 a.m.
Created at: March 6, 2026, 9:48 p.m.