Triple

T2523994
Position Surface form Disambiguated ID Type / Status
Subject Negros Occidental E55991 entity
Predicate hasCity P316 FINISHED
Object Talisay
Talisay is a city in the Philippine province of Negros Occidental known for its sugarcane industry and historical landmarks.
E385024 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: Talisay | Statement: [Negros Occidental, hasCity, Talisay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Talisay
Context triple: [Negros Occidental, hasCity, Talisay]
  • A. Talisay City
    Talisay City is a coastal component city in the province of Cebu in the Philippines, known for its historical significance and proximity to Metro Cebu.
  • B. Sagay
    Sagay is a coastal city in the province of Negros Occidental in the Philippines, known for its rich marine resources and protected seascape.
  • C. Guihulngan
    Guihulngan is a coastal city and commercial hub in the northern part of Negros Oriental in the Philippines.
  • D. Moalboal
    Moalboal is a coastal town in the Philippines renowned for its vibrant coral reefs, sardine runs, and popular diving and snorkeling spots.
  • E. Argao
    Argao is a coastal municipality in the southeastern part of Cebu, Philippines, known for its Spanish-era heritage structures and traditional delicacies.
  • 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: Talisay
Triple: [Negros Occidental, hasCity, Talisay]
Generated description
Talisay is a city in the Philippine province of Negros Occidental known for its sugarcane industry and historical landmarks.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Talisay
Target entity description: Talisay is a city in the Philippine province of Negros Occidental known for its sugarcane industry and historical landmarks.
  • A. Talisay City
    Talisay City is a coastal component city in the province of Cebu in the Philippines, known for its historical significance and proximity to Metro Cebu.
  • B. Sagay
    Sagay is a coastal city in the province of Negros Occidental in the Philippines, known for its rich marine resources and protected seascape.
  • C. Guihulngan
    Guihulngan is a coastal city and commercial hub in the northern part of Negros Oriental in the Philippines.
  • D. Moalboal
    Moalboal is a coastal town in the Philippines renowned for its vibrant coral reefs, sardine runs, and popular diving and snorkeling spots.
  • E. Argao
    Argao is a coastal municipality in the southeastern part of Cebu, Philippines, known for its Spanish-era heritage structures and traditional delicacies.
  • 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_69ab4a48e4f081908f1218d244608659 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd252f1c88190ac93604542f80f49 completed March 7, 2026, 7:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69b4dae21cf08190913e7b764b3acb20 completed March 14, 2026, 3:49 a.m.
NEDg Description generation batch_69b4dc2c274c8190a7c8fa419186e188 completed March 14, 2026, 3:55 a.m.
NED2 Entity disambiguation (via description) batch_69b4e0065b008190b2ad78546e563b4d completed March 14, 2026, 4:11 a.m.
Created at: March 6, 2026, 9:46 p.m.