Triple
T4242275
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sierra Madre |
E95440
|
entity |
| Predicate | province |
P604
|
FINISHED |
| Object | Aurora |
E188575
|
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: [Sierra Madre, province, Aurora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aurora Context triple: [Sierra Madre, province, Aurora]
-
A.
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.
-
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
chosen
Aurora is a coastal province in the Philippines known for its Pacific shoreline, surfing spots like Baler, and lush mountainous landscapes.
-
D.
Aurora
Aurora was a Russian protected cruiser famed for firing the symbolic shot that signaled the start of the October Revolution in 1917.
-
E.
Aurora
Aurora is a central character in Manuel Puig’s novel and its adaptations "Kiss of the Spider Woman," known as the glamorous, enigmatic film actress idolized and retold through the protagonist’s stories.
- 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_69b3453d91548190b4d4ef8fe52aa2ac |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34e891bc08190831187da4f553f48 |
completed | March 12, 2026, 11:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5a872fd6881908a3fbe37e7c35c92 |
completed | March 14, 2026, 6:26 p.m. |
Created at: March 12, 2026, 11:05 p.m.