Triple
T13391828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Whiting Beach |
E319591
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Yamba |
E52273
|
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: Yamba | Statement: [Whiting Beach, locatedIn, Yamba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yamba Context triple: [Whiting Beach, locatedIn, Yamba]
-
A.
Yamba
chosen
Yamba is a coastal town in northern New South Wales, Australia, known for its beaches, fishing, and laid-back holiday atmosphere.
-
B.
Mudjimba
Mudjimba is a coastal suburb on Queensland’s Sunshine Coast in Australia, known for its beaches and relaxed seaside atmosphere.
-
C.
Jamberoo
Jamberoo is a small rural village in the Illawarra region of New South Wales, Australia, known for its dairy farming heritage and the nearby Jamberoo Action Park.
-
D.
Boorara
Boorara is a small locality in Western Australia known historically for its association with gold mining in the Eastern Goldfields region.
-
E.
Alwina
Alwina is the naive Tunisian shepherdess who becomes a sophisticated Parisian socialite in the 1935 French film "Princesse Tam-Tam."
- 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_69d806b886bc8190b676e7768b8e01c5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dba0d74e5881909828854bba7d9a87 |
completed | April 12, 2026, 1:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7942242cc8190aa94efae75370328 |
completed | May 3, 2026, 6:29 p.m. |
Created at: April 9, 2026, 9:34 p.m.