Triple

T4058664
Position Surface form Disambiguated ID Type / Status
Subject Tarlac E84755 entity
Predicate hasCity P316 FINISHED
Object Bamban
Bamban is a municipality in the province of Tarlac in the Philippines, known for its proximity to Mount Pinatubo and its role in the region’s post-eruption development and eco-tourism.
 E417504 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: Bamban | Statement: [Tarlac, hasCity, Bamban]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bamban
Context triple: [Tarlac, hasCity, Bamban]
  • A. Sipalay
    Sipalay is a coastal city in Negros Occidental, Philippines, known for its beaches, diving spots, and laid-back tourism.
  • B. Balamban
    Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
  • C. Pulilan
    Pulilan is a municipality in the province of Bulacan in the Philippines, known for its agricultural economy and the annual Kneeling Carabao Festival honoring San Isidro Labrador.
  • D. Balanga
    Balanga is a coastal city in the province of Bataan in the Philippines, situated along the shores of Manila Bay.
  • E. Sagay
    Sagay is a coastal city in the province of Negros Occidental in the Philippines, known for its rich marine resources and protected seascape.
  • 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: Bamban
Triple: [Tarlac, hasCity, Bamban]
Generated description
Bamban is a municipality in the province of Tarlac in the Philippines, known for its proximity to Mount Pinatubo and its role in the region’s post-eruption development and eco-tourism.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bamban
Target entity description: Bamban is a municipality in the province of Tarlac in the Philippines, known for its proximity to Mount Pinatubo and its role in the region’s post-eruption development and eco-tourism.
  • A. Sipalay
    Sipalay is a coastal city in Negros Occidental, Philippines, known for its beaches, diving spots, and laid-back tourism.
  • B. Balamban
    Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
  • C. Pulilan
    Pulilan is a municipality in the province of Bulacan in the Philippines, known for its agricultural economy and the annual Kneeling Carabao Festival honoring San Isidro Labrador.
  • D. Balanga
    Balanga is a coastal city in the province of Bataan in the Philippines, situated along the shores of Manila Bay.
  • E. Sagay
    Sagay is a coastal city in the province of Negros Occidental in the Philippines, known for its rich marine resources and protected seascape.
  • 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_69aed933bec881909edfa28ebb69c634 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefbd13b4481908f9c09cc4f4a9724 completed March 9, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69b57f18218881908c95266c6b18ad47 completed March 14, 2026, 3:30 p.m.
NEDg Description generation batch_69b57fad66e08190a973186c19659ee8 completed March 14, 2026, 3:33 p.m.
NED2 Entity disambiguation (via description) batch_69b58028e7108190a6c92fc9ea300f9e completed March 14, 2026, 3:35 p.m.
Created at: March 9, 2026, 3:38 p.m.