Triple
T10579075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clarence Valley |
E249687
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Angourie |
E264988
|
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: Angourie | Statement: [Clarence Valley, contains, Angourie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Angourie Context triple: [Clarence Valley, contains, Angourie]
-
A.
Angourie
chosen
Angourie is a small coastal village in New South Wales, Australia, renowned for its surf breaks and scenic beaches.
-
B.
Katisha
Katisha is a formidable, older noblewoman and comic villainess in Gilbert and Sullivan’s operetta "The Mikado," known for her dramatic presence and unrequited love for Nanki-Poo.
-
C.
Sheilia
Sheilia is a feminine given name, typically considered an alternative spelling of the name Sheila.
-
D.
Mia Ausa
Mia Ausa is a young, kind-hearted magician and the daughter of the Magic Guild's leader in the role-playing game Lunar: The Silver Star.
-
E.
Malalai
Malalai is an Afghan activist and former politician internationally recognized for her outspoken criticism of warlords, the Taliban, and foreign occupation in Afghanistan.
- 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_69d381c9d3d48190a29ee491e1696a0e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d52757b870819085b03aa6805aa076 |
completed | April 7, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d94b6f43f4819092557d1c6039324a |
completed | April 10, 2026, 7:11 p.m. |
Created at: April 6, 2026, 12:38 p.m.