Triple
T14904351
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nevertire |
E360088
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Orana |
E319350
|
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: Orana | Statement: [Nevertire, region, Orana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orana Context triple: [Nevertire, region, Orana]
-
A.
Orana
chosen
Orana is a large, predominantly rural region in inland New South Wales, Australia, known for its agricultural production and regional service centers such as Dubbo.
-
B.
Alwina
Alwina is the naive Tunisian shepherdess who becomes a sophisticated Parisian socialite in the 1935 French film "Princesse Tam-Tam."
-
C.
Ouyen
Ouyen is a small rural town in northwestern Victoria, Australia, known historically as a service centre for the surrounding Mallee farming region.
-
D.
Winya
Winya is a small rural town located within Queensland’s Somerset Region in Australia.
-
E.
Tullamarine
Tullamarine is a suburb in Melbourne, Australia, best known for being the location of Melbourne Airport.
- 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_69d827980cbc8190a0c569ae3940a1d9 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69ded60cd5588190b1efecc2b220da69 |
completed | April 15, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe72b4e4f88190af7e859d93dbbd28 |
completed | May 8, 2026, 11:33 p.m. |
Created at: April 10, 2026, 2:12 a.m.