Triple
T5317745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kingdom |
E121591
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Aurora |
E285207
|
NE FINISHED |
How this triple was built (2 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: [Kingdom, hasCharacter, Aurora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aurora Context triple: [Kingdom, hasCharacter, Aurora]
-
A.
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.
-
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 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.
-
D.
Aurora
chosen
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.
-
E.
Aurora
Aurora was a Russian protected cruiser famed for firing the symbolic shot that signaled the start of the October Revolution in 1917.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69bd463d956c819088105c3db802c017 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd855269ac8190bb7a9248d04f1823 |
completed | March 20, 2026, 5:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf188d3a2c8190ad06d1b71ef73780 |
completed | March 21, 2026, 10:15 p.m. |
Created at: March 20, 2026, 1:59 p.m.