Triple
T15228944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Region IV-A |
E363946
|
entity |
| Predicate | bordersProvince |
P224
|
FINISHED |
| Object |
Aurora
Aurora is a coastal province in the Philippines known for its Pacific shoreline, surfing spots, and lush 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: [Region IV-A, bordersProvince, Aurora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aurora Context triple: [Region IV-A, bordersProvince, 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: [Region IV-A, bordersProvince, Aurora]
Generated description
Aurora is a coastal province in the Philippines known for its Pacific shoreline, surfing spots, and lush 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 lush 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0078ccdf48190b34eabd9e24e45a1 |
completed | April 15, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fedd39d42881908f2ad47613e23bfa |
completed | May 9, 2026, 7:07 a.m. |
| NEDg | Description generation | batch_69fedf277f888190a7e218131740660d |
completed | May 9, 2026, 7:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fedfa514808190b4a50e87833c5f69 |
completed | May 9, 2026, 7:17 a.m. |
Created at: April 10, 2026, 3:12 a.m.