Triple
T389950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richard Pryor |
E8858
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Car Wash
Car Wash is a 1976 comedy film set in a Los Angeles car wash, known for its ensemble cast, funky soundtrack, and satirical look at working-class life.
|
E49199
|
NE FINISHED |
How this triple was built (4 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: Car Wash | Statement: [Richard Pryor, notableWork, Car Wash]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Car Wash Context triple: [Richard Pryor, notableWork, Car Wash]
-
A.
Ventra
Ventra is the contactless fare payment system used across Chicago’s public transit network, including buses and trains.
-
B.
Byfleet
Byfleet is a village and former civil parish in southeast England, situated within the county of Surrey.
-
C.
Red Duster
The Red Duster is the traditional British civil ensign, a red flag with the Union Jack in the canton historically flown by British merchant ships.
-
D.
Revs
Revs is the commonly used nickname for the New England Revolution, a professional Major League Soccer club based in the Greater Boston area.
-
E.
Chance Rides
Chance Rides is an American amusement ride manufacturer known for producing Ferris wheels, carousels, and other attractions for theme parks and entertainment venues worldwide.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Car Wash Triple: [Richard Pryor, notableWork, Car Wash]
Generated description
Car Wash is a 1976 comedy film set in a Los Angeles car wash, known for its ensemble cast, funky soundtrack, and satirical look at working-class life.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Car Wash Target entity description: Car Wash is a 1976 comedy film set in a Los Angeles car wash, known for its ensemble cast, funky soundtrack, and satirical look at working-class life.
-
A.
Ventra
Ventra is the contactless fare payment system used across Chicago’s public transit network, including buses and trains.
-
B.
Byfleet
Byfleet is a village and former civil parish in southeast England, situated within the county of Surrey.
-
C.
Red Duster
The Red Duster is the traditional British civil ensign, a red flag with the Union Jack in the canton historically flown by British merchant ships.
-
D.
Revs
Revs is the commonly used nickname for the New England Revolution, a professional Major League Soccer club based in the Greater Boston area.
-
E.
Chance Rides
Chance Rides is an American amusement ride manufacturer known for producing Ferris wheels, carousels, and other attractions for theme parks and entertainment venues worldwide.
- F. None of above. chosen
Provenance (5 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_69a2e7f55c60819097aff65ea2ca2832 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec5bdc848190826701590070497b |
completed | Feb. 28, 2026, 1:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4035310608190a1e0f807cb93e1e1 |
completed | March 1, 2026, 9:13 a.m. |
| NEDg | Description generation | batch_69a403a7c1488190a7773a5ae8a8cec7 |
completed | March 1, 2026, 9:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a40428b014819091c6534ba35a11ff |
completed | March 1, 2026, 9:17 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.