Triple
T24902781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rebound Ace |
E623623
|
entity |
| Predicate | typicalCourtColor |
P104905
|
FINISHED |
| Object | green court with blue or green surrounds |
—
|
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: green court with blue or green surrounds | Statement: [Rebound Ace, typicalCourtColor, green court with blue or green surrounds]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCourtColor Context triple: [Rebound Ace, typicalCourtColor, green court with blue or green surrounds]
-
A.
courtColor
chosen
Indicates the color characteristic assigned to or associated with a court.
-
B.
suitColor
Indicates that one entity has or is associated with a particular color of suit.
-
C.
traditionalKitColor
Indicates the customary or historically established color scheme used for an entity’s standard or primary kit or uniform.
-
D.
dressColor
Indicates the color attribute associated with a particular dress.
-
E.
hoodColor
Indicates the color attribute specifically of an entity’s hood (e.g., the hood of a garment or vehicle).
- 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_69e2fac797cc8190b30d77f4121099ac |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f6f8565134819096aac0175f924a9f |
completed | May 3, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69f6f65fd1d08190b88e5e68ba268500 |
completed | May 3, 2026, 7:16 a.m. |
Created at: April 18, 2026, 5:27 a.m.