Triple
T3814665
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daytona International Speedway |
E84221
|
entity |
| Predicate | hasNumberOfVideoBoards |
P2426
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Daytona International Speedway, hasNumberOfVideoBoards, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfVideoBoards Context triple: [Daytona International Speedway, hasNumberOfVideoBoards, 3]
-
A.
hasVideoBoard
Indicates that an entity is equipped with or includes a video board (graphics/display hardware component).
-
B.
hasNumberOfPlatforms
Indicates the relationship that specifies how many platforms are associated with a given entity.
-
C.
hasNumberOfScreens
chosen
Indicates the quantity of screens associated with or contained in a given entity.
-
D.
hasOnboardLEDs
Indicates that one entity is equipped with built-in LED lights as part of its hardware.
-
E.
numberOfAreaBoards
Indicates the total count of area boards associated with or defined for a given entity or context.
- 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_69aed931f5908190be2c07af66d4df25 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef1515c688190a38332aedeed8a76 |
completed | March 9, 2026, 4:12 p.m. |
| PD | Predicate disambiguation | batch_69aee7482d708190a3ec74745b102a4c |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:17 p.m.