Triple
T35330721
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rowdy |
E1020307
|
entity |
| Predicate | associatedWithCarNumber |
P106324
|
FINISHED |
| Object | 18 |
—
|
LITERAL 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: 18 | Statement: [Rowdy, associatedWithCarNumber, 18]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithCarNumber Context triple: [Rowdy, associatedWithCarNumber, 18]
-
A.
associatedWithDriver
Indicates that one entity has a connection or relationship with a specific driver, such as being linked, assigned, or otherwise related to that driver.
-
B.
alsoAssociatedWithDriver
Indicates that an entity has an additional or secondary association with a specified driver, beyond any primary or previously stated driver relationship.
-
C.
associatedWithVehicleOrStructure
Indicates that an entity has a relevant connection or involvement with a particular vehicle or structure.
-
D.
carNumberUsed
chosen
Indicates that a specific car number has been used or assigned in a given context or event.
-
E.
associatedWithPark
Indicates a relationship in which an entity is connected or linked in some relevant way to a particular park.
- F. None of above.
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_69f76deacf4481908e7735a5a7715b0a |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c8c34f88190ace26f555827f23e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0324d08190ac5b610cc0f6a38c |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:03 p.m.