Triple

T16979137
Position Surface form Disambiguated ID Type / Status
Subject Santa Ignacia E411894 entity
Predicate hasBarangay P29835 FINISHED
Object Santa Ines
Santa Ines is a barangay (village-level administrative division) within the municipality of Santa Ignacia in the Philippines.
E1244841 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: Santa Ines | Statement: [Santa Ignacia, hasBarangay, Santa Ines]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Santa Ines
Context triple: [Santa Ignacia, hasBarangay, Santa Ines]
  • A. Santa Isabel
    Santa Isabel is a supermarket chain in Latin America operated under the retail group Cencosud.
  • B. Santa Isabel
    Santa Isabel is a coastal municipality in southern Puerto Rico known for its agricultural production, particularly sugarcane and plantains.
  • C. Santa Isabel
    Santa Isabel is a municipality in the state of São Paulo, Brazil, known for its preserved natural areas and role as part of the greater São Paulo region.
  • D. Santa Isabel
    Santa Isabel is a Santiago Metro station on Line 5 located in the central area of Chile’s capital city.
  • E. Santa Isabel
    Santa Isabel is an urban neighborhood within the Carabayllo district of Lima, Peru.
  • 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: Santa Ines
Triple: [Santa Ignacia, hasBarangay, Santa Ines]
Generated description
Santa Ines is a barangay (village-level administrative division) within the municipality of Santa Ignacia in the Philippines.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Santa Ines
Target entity description: Santa Ines is a barangay (village-level administrative division) within the municipality of Santa Ignacia in the Philippines.
  • A. Santa Isabel
    Santa Isabel was a Spanish expedition ship associated with the Santa Cruz colony during the era of New World exploration.
  • B. Santa Isabel
    Santa Isabel is a supermarket chain in Latin America operated under the retail group Cencosud.
  • C. Santa Isabel
    Santa Isabel is a town in southern Ecuador’s Azuay Province, known as a local commercial and agricultural center in the region.
  • D. Santa Isabel
    Santa Isabel is a municipality in the state of São Paulo, Brazil, known for its preserved natural areas and role as part of the greater São Paulo region.
  • E. Santa Isabel
    Santa Isabel is a Santiago Metro station on Line 5 located in the central area of Chile’s capital city.
  • 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_69d886ca8f348190812768ea8d5055ce completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d185a9408190a991bf8a1ef694f0 completed April 18, 2026, 6:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00dc0d437c81908f003a10b798998a completed May 10, 2026, 7:27 p.m.
NEDg Description generation batch_6a0114d33cac819083d8e542ea5bc274 completed May 10, 2026, 11:29 p.m.
NED2 Entity disambiguation (via description) batch_6a0115c967b0819088e2335fd45d755b completed May 10, 2026, 11:33 p.m.
Created at: April 10, 2026, 5:32 a.m.