Triple
T14766890
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olympic Green Tennis Center |
E347018
|
entity |
| Predicate | numberOfTennisCourts |
P4502
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [Olympic Green Tennis Center, numberOfTennisCourts, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTennisCourts Context triple: [Olympic Green Tennis Center, numberOfTennisCourts, 10]
-
A.
numberOfCourts
chosen
Indicates the quantity of courts associated with or present at a given entity or location.
-
B.
hasTennisCourt
Indicates that one entity possesses, includes, or provides access to a tennis court as part of its facilities or attributes.
-
C.
hasIndoorCourts
Indicates that one entity provides or contains courts or playing areas that are located indoors.
-
D.
hasCourtSurface
Indicates that something (such as a court or playing area) possesses a specific type of surface.
-
E.
hasMainCourts
Indicates that an entity possesses or is associated with one or more primary courts used for official or main activities.
- 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7f576c881909da70627f5897c94 |
completed | April 14, 2026, 11:04 p.m. |
| PD | Predicate disambiguation | batch_69de8c02e5c08190943c27594026faf7 |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:30 a.m.