Triple

T11572933
Position Surface form Disambiguated ID Type / Status
Subject Oton E274434 entity
Predicate hasBarangay P29835 FINISHED
Object Cau-ayan
Cau-ayan is a barangay (village-level administrative division) within the municipality of Oton in the province of Iloilo, Philippines.
E935736 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: Cau-ayan | Statement: [Oton, hasBarangay, Cau-ayan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cau-ayan
Context triple: [Oton, hasBarangay, Cau-ayan]
  • A. Anahawan
    Anahawan is a coastal municipality in the province of Southern Leyte in the Philippines, known for its rural communities and agricultural economy.
  • B. Tanauan
    Tanauan is a city in the Calabarzon region of the Philippines known for its growing industrial zones and proximity to Metro Manila.
  • C. Maljamar
    Maljamar is a small unincorporated community in southeastern New Mexico known historically for its oil and gas activity.
  • D. Mayong
    Mayong is a village in Assam, India, historically renowned as the "land of black magic" and associated with numerous myths, occult practices, and tantric traditions.
  • E. Daanbantayan
    Daanbantayan is a northern coastal municipality in the Philippine province of Cebu known as a gateway to popular diving and beach destinations like Malapascua Island.
  • 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: Cau-ayan
Triple: [Oton, hasBarangay, Cau-ayan]
Generated description
Cau-ayan is a barangay (village-level administrative division) within the municipality of Oton in the province of Iloilo, Philippines.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Cau-ayan
Target entity description: Cau-ayan is a barangay (village-level administrative division) within the municipality of Oton in the province of Iloilo, Philippines.
  • A. Anahawan
    Anahawan is a coastal municipality in the province of Southern Leyte in the Philippines, known for its rural communities and agricultural economy.
  • B. Tanauan
    Tanauan is a city in the Calabarzon region of the Philippines known for its growing industrial zones and proximity to Metro Manila.
  • C. Maljamar
    Maljamar is a small unincorporated community in southeastern New Mexico known historically for its oil and gas activity.
  • D. Mayong
    Mayong is a village in Assam, India, historically renowned as the "land of black magic" and associated with numerous myths, occult practices, and tantric traditions.
  • E. Daanbantayan
    Daanbantayan is a northern coastal municipality in the Philippine province of Cebu known as a gateway to popular diving and beach destinations like Malapascua Island.
  • 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_69d6aae5ac3c81908d2b0a3a665665b2 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d88dd6913881908becf188c0a7a275 completed April 10, 2026, 5:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69e8a776ea40819084912ad2459c5be9 completed April 22, 2026, 10:48 a.m.
NEDg Description generation batch_69e8aa378edc8190807451b1855a4502 completed April 22, 2026, 11 a.m.
NED2 Entity disambiguation (via description) batch_69e8b05117988190aa029efa250053db completed April 22, 2026, 11:26 a.m.
Created at: April 8, 2026, 9:38 p.m.