Triple

T10632969
Position Surface form Disambiguated ID Type / Status
Subject Aklan E250504 entity
Predicate hasMunicipality P847 FINISHED
Object Ibajay
Ibajay is a coastal municipality in the Philippine province of Aklan known for its mangrove forest and agricultural communities.
 E876376 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: Ibajay | Statement: [Aklan, hasMunicipality, Ibajay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ibajay
Context triple: [Aklan, hasMunicipality, Ibajay]
  • A. Kitaakita
    Kitaakita is a city in northern Japan known for its mountainous landscapes, hot springs, and traditional festivals within Akita Prefecture.
  • B. Ibaan
    Ibaan is a landlocked municipality in the province of Batangas in the Philippines, known for its agricultural economy and local religious and cultural festivities.
  • C. Kayabacho
    Kayabacho is a commercial district in Tokyo's Chūō ward known as a financial hub with dense office buildings and convenient subway access.
  • D. Sarangani
    Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
  • E. Balamban
    Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
  • 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: Ibajay
Triple: [Aklan, hasMunicipality, Ibajay]
Generated description
Ibajay is a coastal municipality in the Philippine province of Aklan known for its mangrove forest and agricultural communities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ibajay
Target entity description: Ibajay is a coastal municipality in the Philippine province of Aklan known for its mangrove forest and agricultural communities.
  • A. Kitaakita
    Kitaakita is a city in northern Japan known for its mountainous landscapes, hot springs, and traditional festivals within Akita Prefecture.
  • B. Ibaan
    Ibaan is a landlocked municipality in the province of Batangas in the Philippines, known for its agricultural economy and local religious and cultural festivities.
  • C. Kayabacho
    Kayabacho is a commercial district in Tokyo's Chūō ward known as a financial hub with dense office buildings and convenient subway access.
  • D. Sarangani
    Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
  • E. Balamban
    Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
  • 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_69d6aa5993448190a493b790b8f85010 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6df95f5e88190b34ce3ec972759ef completed April 8, 2026, 11:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69d96bbd64d8819089d55af875d39e45 completed April 10, 2026, 9:29 p.m.
NEDg Description generation batch_69d9701de92881908c0b8f05eae97e35 completed April 10, 2026, 9:48 p.m.
NED2 Entity disambiguation (via description) batch_69d970f3f78081909bcb2dae6dae06d5 completed April 10, 2026, 9:51 p.m.
Created at: April 8, 2026, 9:02 p.m.