Triple

T3770838
Position Surface form Disambiguated ID Type / Status
Subject Pampanga E83192 entity
Predicate hasMunicipality P847 FINISHED
Object Floridablanca
Floridablanca is a landlocked agricultural municipality in the province of Pampanga in the Philippines, known for its rice fields and rural communities.
 E386370 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: Floridablanca | Statement: [Pampanga, hasMunicipality, Floridablanca]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Floridablanca
Context triple: [Pampanga, hasMunicipality, Floridablanca]
  • A. Floridablanca
    Floridablanca is a rapidly growing city in northeastern Colombia known for its proximity to Bucaramanga and its mix of residential areas, commerce, and tourism.
  • B. Naranjal
    Naranjal is a town and canton in southwestern Ecuador known for its agricultural production and location within Guayas Province.
  • C. Gorbea
    Gorbea is a small Chilean municipality and town located in the Araucanía Region, known for its agricultural activities and rural character.
  • D. Azaña
    Azaña is the surname of Manuel Azaña, a prominent Spanish politician and writer who served as President of the Second Spanish Republic.
  • E. Molinero
    Molinero is a Spanish surname that corresponds to the German surname Müller, both historically referring to the occupation of a miller.
  • 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: Floridablanca
Triple: [Pampanga, hasMunicipality, Floridablanca]
Generated description
Floridablanca is a landlocked agricultural municipality in the province of Pampanga in the Philippines, known for its rice fields and rural communities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Floridablanca
Target entity description: Floridablanca is a landlocked agricultural municipality in the province of Pampanga in the Philippines, known for its rice fields and rural communities.
  • A. Floridablanca
    Floridablanca is a rapidly growing city in northeastern Colombia known for its proximity to Bucaramanga and its mix of residential areas, commerce, and tourism.
  • B. Naranjal
    Naranjal is a town and canton in southwestern Ecuador known for its agricultural production and location within Guayas Province.
  • C. Gorbea
    Gorbea is a small Chilean municipality and town located in the Araucanía Region, known for its agricultural activities and rural character.
  • D. Azaña
    Azaña is the surname of Manuel Azaña, a prominent Spanish politician and writer who served as President of the Second Spanish Republic.
  • E. Molinero
    Molinero is a Spanish surname that corresponds to the German surname Müller, both historically referring to the occupation of a miller.
  • 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_69ad8b235e608190b5a2b1d1bfcef50b completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcc307cf8819090730b5e697bb197 completed March 8, 2026, 7:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4e5287908819084319b8dfa407635 completed March 14, 2026, 4:33 a.m.
NEDg Description generation batch_69b4e61c8dc881908e298528b1e42c0c completed March 14, 2026, 4:37 a.m.
NED2 Entity disambiguation (via description) batch_69b4e686bf2c8190aac01d6c1014c1d4 completed March 14, 2026, 4:39 a.m.
Created at: March 8, 2026, 3:36 p.m.