Triple
T12426203
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paula Gosling |
E296903
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Ricochet |
E371109
|
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: Ricochet | Statement: [Paula Gosling, notableWork, Ricochet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ricochet Context triple: [Paula Gosling, notableWork, Ricochet]
-
A.
Ricochet
chosen
Ricochet is a 1991 action thriller film in which John Lithgow plays a sadistic criminal seeking revenge on a cop who put him behind bars.
-
B.
Ricochet
Ricochet is a wild mouse–style steel roller coaster known for its sharp turns and sudden drops at the Carowinds amusement park.
-
C.
Ricochet
Ricochet was an early wireless internet service network developed by Metricom that provided mobile, high-speed data access in urban areas before Wi-Fi and modern cellular data became widespread.
-
D.
Bounce
"Bounce" is a popular electro house track by Canadian electronic music duo MSTRKRFT, known for its heavy synths and club-oriented energy.
-
E.
Bounce
"Bounce" is a high-energy dance-pop and Bollywood-influenced single by Australian rapper Iggy Azalea, known for its vibrant production and colorful music video.
- 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_69d6ada0640c81908c061d7fb3d47786 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d7ccda08190be2ff1739c1c6855 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63499061881908d25af7c3474f774 |
completed | May 2, 2026, 5:30 p.m. |
Created at: April 8, 2026, 9:55 p.m.