Triple

T21975320
Position Surface form Disambiguated ID Type / Status
Subject Province of Bulacan E542689 entity
Predicate borders P224 FINISHED
Object Aurora
Aurora is a coastal province in the Philippines known for its Pacific shoreline, surfing spots, and mountainous landscapes.
 E188575 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: Aurora | Statement: [Province of Bulacan, borders, Aurora]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Aurora
Context triple: [Province of Bulacan, borders, Aurora]
  • A. Aurora
    Aurora is a suburban town in central York Region, Ontario, known as an affluent residential community within the Greater Toronto Area.
  • B. Aurora
    Aurora was an influential late-18th-century American newspaper edited by Benjamin Franklin Bache that was known for its strong Republican stance and criticism of Federalist policies.
  • C. Aurora
    Aurora is a residential neighborhood located within the Algiers area of New Orleans, Louisiana.
  • D. Aurora
    Aurora was a common Filipino female given name in the early 20th century, notably borne by Aurora Aragon Quezon, the First Lady of the Philippines.
  • E. Aurora
    Aurora is a feminine given name of Latin origin meaning "dawn," famously borne by figures in mythology, royalty, and popular culture.
  • 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: Aurora
Triple: [Province of Bulacan, borders, Aurora]
Generated description
Aurora is a coastal province in the Philippines known for its Pacific shoreline, surfing spots, and mountainous landscapes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Aurora
Target entity description: Aurora is a coastal province in the Philippines known for its Pacific shoreline, surfing spots, and mountainous landscapes.
  • A. Aurora chosen
    Aurora is a coastal province in the Philippines known for its Pacific shoreline, surfing spots like Baler, and lush mountainous landscapes.
  • B. Aurora
    Aurora is a suburban town in central York Region, Ontario, known as an affluent residential community within the Greater Toronto Area.
  • C. Aurora
    Aurora is a major suburban city in the Denver metropolitan area of Colorado, known for its diverse population, extensive parks and open spaces, and role as a key economic and residential hub on the eastern side of the metro region.
  • D. Aurora
    Aurora was a common Filipino female given name in the early 20th century, notably borne by Aurora Aragon Quezon, the First Lady of the Philippines.
  • E. Aurora
    Aurora is a major city in northeastern Illinois, known as a key suburb of Chicago and a regional center for industry, transportation, and technology.
  • F. None of above.

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_69e0c48070988190909db97667b9a0ac completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f12487a1a88190abb8a51fcd533b6a completed April 28, 2026, 9:20 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a671abb648190b9a4cc67c733f356 completed May 18, 2026, 1:10 a.m.
NEDg Description generation batch_6a0a6812112c81909844ab725dff79ed completed May 18, 2026, 1:14 a.m.
NED2 Entity disambiguation (via description) batch_6a0a68846fb88190bbe1510608aa9468 completed May 18, 2026, 1:16 a.m.
Created at: April 16, 2026, 8:03 p.m.