Triple
T10390946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ron’s Gone Wrong |
E244889
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Lara Breay |
E372947
|
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: Lara Breay | Statement: [Ron’s Gone Wrong, producer, Lara Breay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lara Breay Context triple: [Ron’s Gone Wrong, producer, Lara Breay]
-
A.
Lara Breay
chosen
Lara Breay is a film producer best known for her work on the animated superhero comedy "Megamind."
-
B.
Lara Stone
Lara Stone is a Dutch fashion model renowned for her distinctive gap-toothed look and work with major luxury brands and magazines.
-
C.
Lara Marlowe
Lara Marlowe is an American-born journalist and longtime foreign correspondent, best known for her reporting from the Middle East and Europe for outlets such as The Irish Times.
-
D.
Lara Worthington
Lara Worthington is an Australian model and media personality best known for her work in fashion campaigns and reality television, as well as her high-profile public image.
-
E.
Bayta Darell
Bayta Darell is a pivotal character in Isaac Asimov's Foundation series, known for her crucial role in thwarting the Mule's conquest in "Foundation and Empire."
- 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_69d381b5116081908d85227bab6d3c0c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9b4f7d08190bcb16d3b4c8f22ad |
completed | April 7, 2026, 11:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d795b9974c819087340adc3622279e |
completed | April 9, 2026, 12:04 p.m. |
Created at: April 6, 2026, 12:06 p.m.