Triple

T12320769
Position Surface form Disambiguated ID Type / Status
Subject Mt. Matutum E293721 entity
Predicate locatedIn P40 FINISHED
Object Sarangani
Sarangani is a province in the southern Philippines known for its coastal landscapes, rich marine biodiversity, and proximity to prominent natural landmarks like Mt. Matutum.
 E257340 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: Sarangani | Statement: [Mt. Matutum, locatedIn, Sarangani]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sarangani
Context triple: [Mt. Matutum, locatedIn, Sarangani]
  • A. Sarangani
    Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
  • B. Danao
    Danao is a coastal city and municipality on Cebu Island in the Philippines known for its historical significance and local industries.
  • C. Marawila
    Marawila is a coastal town in Sri Lanka known for its beaches, fishing community, and tourism-oriented resorts.
  • D. Karagawan
    Karagawan is a regional dialect of the Isnag language spoken by the Isnag people of northern Luzon in the Philippines.
  • E. Maragondon
    Maragondon is a historic rural municipality in the province of Cavite in the Philippines, known for its Spanish-era heritage sites and nearby natural attractions.
  • 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: Sarangani
Triple: [Mt. Matutum, locatedIn, Sarangani]
Generated description
Sarangani is a province in the southern Philippines known for its coastal landscapes, rich marine biodiversity, and proximity to prominent natural landmarks like Mt. Matutum.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sarangani
Target entity description: Sarangani is a province in the southern Philippines known for its coastal landscapes, rich marine biodiversity, and proximity to prominent natural landmarks like Mt. Matutum.
  • A. Sarangani chosen
    Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
  • B. Danao
    Danao is a coastal city and municipality on Cebu Island in the Philippines known for its historical significance and local industries.
  • C. Marawila
    Marawila is a coastal town in Sri Lanka known for its beaches, fishing community, and tourism-oriented resorts.
  • D. Karagawan
    Karagawan is a regional dialect of the Isnag language spoken by the Isnag people of northern Luzon in the Philippines.
  • E. Maragondon
    Maragondon is a historic rural municipality in the province of Cavite in the Philippines, known for its Spanish-era heritage sites and nearby natural attractions.
  • F. None of above.

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_69d6ab6ae0dc8190b1522a9c1c55c114 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f4c2b548190938fff9427f07dc7 completed April 10, 2026, 6:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6555e525c8190aa72da362fae1e3e completed May 2, 2026, 7:49 p.m.
NEDg Description generation batch_69f6566dccc0819085e059c7b0288f6c completed May 2, 2026, 7:54 p.m.
NED2 Entity disambiguation (via description) batch_69f657aec8fc8190b3b08ccb95595958 completed May 2, 2026, 7:59 p.m.
Created at: April 8, 2026, 9:53 p.m.