Triple
T15999370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Virtua Tennis |
E388055
|
entity |
| Predicate | featuresMode |
P120704
|
FINISHED |
| Object | single-player |
—
|
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: single-player | Statement: [Virtua Tennis, featuresMode, single-player]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresMode Context triple: [Virtua Tennis, featuresMode, single-player]
-
A.
featuresModel
Indicates that one entity includes, exposes, or is characterized by a particular model as one of its defining components or capabilities.
-
B.
featuresSetting
Indicates that something includes, presents, or highlights a particular setting as a notable or primary aspect.
-
C.
featuresIn
Indicates that an entity appears or plays a role within another entity, such as a person or element being included in a work, event, or context.
-
D.
featuresService
Indicates that one entity provides, includes, or offers a particular service as a notable characteristic or component.
-
E.
featuresSuit
Indicates that one entity includes or presents a particular suit (e.g., clothing, armor, or outfit) as a notable component or attribute.
- F. None of above. chosen
Provenance (4 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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142dc081c819082527e3fa8773460 |
completed | April 16, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69e173af801c8190bfc0f602831bb594 |
completed | April 16, 2026, 11:41 p.m. |
Created at: April 10, 2026, 4:55 a.m.