Triple
T22355695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lara Worthington |
E552646
|
entity |
| Predicate | birthName |
P65
|
FINISHED |
| Object | Lara Bingle |
—
|
NE NERFINISHED |
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: Lara Bingle | Statement: [Lara Worthington, birthName, Lara Bingle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lara Bingle Context triple: [Lara Worthington, birthName, Lara Bingle]
-
A.
Lara Bingle
chosen
Lara Bingle is an Australian model, media personality, and entrepreneur best known for her high-profile advertising campaigns and reality television appearances.
-
B.
Lorraine Adie
Lorraine Adie was a Scottish archaeologist and the mother of drummer and composer Stewart Copeland.
-
C.
Renée Asherson
Renée Asherson was a British stage and film actress known for her delicate, expressive performances in mid-20th-century British cinema and theatre.
-
D.
Fern Britton
Fern Britton is a British television presenter and author best known for her long-running work on daytime TV and her warm, approachable on-screen style.
-
E.
Joan Kempson
Joan Kempson is a British actress known for her character roles in film and television, including a part in the romantic comedy "Fanny and Elvis."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e4a0ad08190a385b4d343cf6524 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f157cf94508190b0f2c63ddfecb813 |
completed | April 29, 2026, 12:58 a.m. |
Created at: April 16, 2026, 8:44 p.m.