Triple
T7276863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angourie Rice |
E163052
|
entity |
| Predicate | givenName |
P17
|
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: [Angourie Rice, givenName, Angourie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Angourie Context triple: [Angourie Rice, givenName, 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.
Naima
Naima is a character in the gospel musical and film "Black Nativity," which reimagines the Nativity story through an African-American cultural and spiritual lens.
-
E.
Naima
"Naima" is a lyrical, modal jazz ballad composed by saxophonist John Coltrane, renowned for its haunting melody and emotional depth.
- 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_69c6885c5964819085b209701769877f |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6eb2f239c819097c1ac4d6de8b0e5 |
completed | March 27, 2026, 8:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7db3110688190bf52180ea159c91c |
completed | March 28, 2026, 1:44 p.m. |
Created at: March 27, 2026, 2:59 p.m.