Triple

T13087933
Position Surface form Disambiguated ID Type / Status
Subject Region III E310385 entity
Predicate includesProvince P11085 FINISHED
Object Aurora
Aurora is a coastal province in the Philippines known for its surfing beaches, rugged Sierra Madre landscapes, and the capital town of Baler.
 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: [Region III, includesProvince, Aurora]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Aurora
Context triple: [Region III, includesProvince, Aurora]
  • A. Aurora
    Aurora is the sleeping princess from Disney's animated film "Sleeping Beauty," known for her grace, kindness, and iconic awakening by true love's kiss.
  • B. Aurora
    Aurora was a Russian protected cruiser famed for firing the symbolic shot that signaled the start of the October Revolution in 1917.
  • C. Aurora
    Aurora is a mystical and theosophical treatise by Jakob Böhme that explores the nature of God, creation, and spiritual rebirth through symbolic and visionary theology.
  • D. Aurora
    "Aurora" is a song by Björk from her 2001 album *Vespertine*, noted for its delicate, atmospheric sound and poetic lyrics.
  • E. Aurora
    Aurora is a Bolivian football club commonly known by its short name, Aurora.
  • 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: [Region III, includesProvince, Aurora]
Generated description
Aurora is a coastal province in the Philippines known for its surfing beaches, rugged Sierra Madre landscapes, and the capital town of Baler.
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 surfing beaches, rugged Sierra Madre landscapes, and the capital town of Baler.
  • 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 is a major city in northeastern Illinois, known as a key suburb of Chicago and a regional center for industry, transportation, and technology.
  • E. 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.
  • 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_69d806a733548190989cfd4ce981ca33 completed April 9, 2026, 8:05 p.m.
NER Named-entity recognition batch_69d981378dd08190b4f00e4e5df0e480 completed April 10, 2026, 11:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6d614704481908758cf8691a941ea completed May 3, 2026, 4:59 a.m.
NEDg Description generation batch_69f6db8b74e08190919245104ac7ebd9 completed May 3, 2026, 5:22 a.m.
NED2 Entity disambiguation (via description) batch_69f6dc4db1e0819082f1b7e196e7f0c0 completed May 3, 2026, 5:25 a.m.
Created at: April 9, 2026, 9:02 p.m.