Triple
T9955029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Club Aurora |
E195425
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
Aurora
Aurora is a Bolivian football club commonly known by its short name, Aurora.
|
E831035
|
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: [Club Aurora, shortName, Aurora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aurora Context triple: [Club Aurora, shortName, 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 is the sleeping princess from Disney's animated film "Sleeping Beauty," known for her grace, kindness, and iconic awakening by true love's kiss.
-
C.
Aurora
Aurora was a Russian protected cruiser famed for firing the symbolic shot that signaled the start of the October Revolution in 1917.
-
D.
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.
-
E.
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.
- 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: [Club Aurora, shortName, Aurora]
Generated description
Aurora is a Bolivian football club commonly known by its short name, Aurora.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Aurora Target entity description: Aurora is a Bolivian football club commonly known by its short name, Aurora.
-
A.
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.
-
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 city in northeastern Illinois, known as a key suburb of Chicago and a regional center for industry, transportation, and technology.
-
D.
Aurora
Aurora is a wealthy, technologically advanced Spacer world in Isaac Asimov’s Robot series, known for its robot-dependent society and pivotal role in the development of human-robot relations.
-
E.
Aurora
Aurora is a coastal province in the Philippines known for its Pacific shoreline, surfing spots like Baler, and lush mountainous landscapes.
- 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_69ca82eaaa008190a54fa1a9f954b9ad |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb69631d08190ab2b1d1c22d46da7 |
completed | April 2, 2026, 12:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2294335208190a0483c4e89abb359 |
completed | April 5, 2026, 9:20 a.m. |
| NEDg | Description generation | batch_69d22b1a3e548190887b46540614536a |
completed | April 5, 2026, 9:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d22bc3ac5c81908b0696ce94263d36 |
completed | April 5, 2026, 9:30 a.m. |
Created at: March 30, 2026, 8:46 p.m.