Triple

T12724240
Position Surface form Disambiguated ID Type / Status
Subject Abra E304062 entity
Predicate hasMunicipality P847 FINISHED
Object Pilar
Pilar is a municipality in the Philippine province of Abra, known for its rural highland landscapes and predominantly agricultural economy.
E1006638 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: Pilar | Statement: [Abra, hasMunicipality, Pilar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pilar
Context triple: [Abra, hasMunicipality, Pilar]
  • A. Pilar
    Pilar is the introspective female protagonist of Paulo Coelho’s novel "By the River Piedra I Sat Down and Wept," whose spiritual and emotional journey drives the story.
  • B. Pilar
    Pilar is a coastal town on Siargao Island in the Philippines, known for its fishing communities and access to popular surfing and eco-tourism spots.
  • C. Pilar
    Pilar is a strong-willed, perceptive Spanish guerrilla fighter who plays a central role in Ernest Hemingway’s novel "For Whom the Bell Tolls."
  • D. Pilar
    Pilar is a riverside city in southwestern Paraguay known for its colonial architecture, river port activities, and proximity to the border with Argentina.
  • E. Pilar
    Pilar is a city in the Buenos Aires Province of Argentina, known as a growing residential and commercial hub within the Greater Buenos Aires metropolitan area.
  • 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: Pilar
Triple: [Abra, hasMunicipality, Pilar]
Generated description
Pilar is a municipality in the Philippine province of Abra, known for its rural highland landscapes and predominantly agricultural economy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Pilar
Target entity description: Pilar is a municipality in the Philippine province of Abra, known for its rural highland landscapes and predominantly agricultural economy.
  • A. Pilar
    Pilar is a coastal town on Siargao Island in the Philippines, known for its fishing communities and access to popular surfing and eco-tourism spots.
  • B. Pilar
    Pilar is a coastal municipality in the Philippine province of Bataan known for its historical significance in World War II and its role in the defense of Bataan.
  • C. Pilar
    Pilar is a city in the Buenos Aires Province of Argentina, known as a growing residential and commercial hub within the Greater Buenos Aires metropolitan area.
  • D. Pilar
    Pilar is a riverside city in southwestern Paraguay known for its colonial architecture, river port activities, and proximity to the border with Argentina.
  • E. Pilar
    Pilar is a Spanish feminine given name, often associated with religious devotion to Our Lady of the Pillar and traditionally used in Spain and Spanish-speaking countries.
  • 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_69d7bdf084148190ab9d513dc0735af4 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d964148f988190a4d0e7b41614fa64 completed April 10, 2026, 8:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69f69b8c84308190b57d3b5b04bb4a78 completed May 3, 2026, 12:49 a.m.
NEDg Description generation batch_69f69d48e6948190a13afe3b8943d877 completed May 3, 2026, 12:56 a.m.
NED2 Entity disambiguation (via description) batch_69f69dfa2b8481908827025a28bfb056 completed May 3, 2026, 12:59 a.m.
Created at: April 9, 2026, 5:25 p.m.