Triple

T19825836
Position Surface form Disambiguated ID Type / Status
Subject Panglao E476322 entity
Predicate hasBarangay P29835 FINISHED
Object Libaong
Libaong is a coastal barangay in the municipality of Panglao in Bohol, Philippines, known for its white-sand beach and tourism activities.
 E1399017 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: Libaong | Statement: [Panglao, hasBarangay, Libaong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Libaong
Context triple: [Panglao, hasBarangay, Libaong]
  • A. Karagawan
    Karagawan is a regional dialect of the Isnag language spoken by the Isnag people of northern Luzon in the Philippines.
  • B. Sarangani
    Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
  • C. Bayan
    Bayan is a traditional Sasak village in northern Lombok, Indonesia, known for its preserved indigenous culture, historic mosques, and role as a gateway to the Mount Rinjani area.
  • D. Bayan
    Bayan is a residential suburb and district located within Kuwait's Hawalli Governorate.
  • E. Malabanias
    Malabanias is a barangay (village-level administrative district) within Angeles City in Pampanga, Philippines, known for its mixed residential, commercial, and entertainment areas.
  • 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: Libaong
Triple: [Panglao, hasBarangay, Libaong]
Generated description
Libaong is a coastal barangay in the municipality of Panglao in Bohol, Philippines, known for its white-sand beach and tourism activities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Libaong
Target entity description: Libaong is a coastal barangay in the municipality of Panglao in Bohol, Philippines, known for its white-sand beach and tourism activities.
  • A. Karagawan
    Karagawan is a regional dialect of the Isnag language spoken by the Isnag people of northern Luzon in the Philippines.
  • B. Sarangani
    Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
  • C. Bayan
    Bayan is a traditional Sasak village in northern Lombok, Indonesia, known for its preserved indigenous culture, historic mosques, and role as a gateway to the Mount Rinjani area.
  • D. Bayan
    Bayan is a residential suburb and district located within Kuwait's Hawalli Governorate.
  • E. Malabanias
    Malabanias is a barangay (village-level administrative district) within Angeles City in Pampanga, Philippines, known for its mixed residential, commercial, and entertainment areas.
  • 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_69d8e51c7c188190b926f3a2a7b5f881 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e656c9e7348190a569a40bd1fca6ba completed April 20, 2026, 4:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a07d43228cc8190a80b7ac4e4ffebca completed May 16, 2026, 2:19 a.m.
NEDg Description generation batch_6a07d7a80f588190a61ff318a4800e10 completed May 16, 2026, 2:34 a.m.
NED2 Entity disambiguation (via description) batch_6a07d89ae8308190a8bc35e303976fea completed May 16, 2026, 2:38 a.m.
Created at: April 10, 2026, 1:50 p.m.