Triple
T6766644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roberta Vinci |
E154735
|
entity |
| Predicate | careerHighDoublesRanking |
P73145
|
FINISHED |
| Object | world No. 1 |
—
|
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: world No. 1 | Statement: [Roberta Vinci, careerHighDoublesRanking, world No. 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerHighDoublesRanking Context triple: [Roberta Vinci, careerHighDoublesRanking, world No. 1]
-
A.
careerDoubles
Indicates the total number of doubles a person has achieved over the course of their entire career.
-
B.
doublesInMLB
Indicates that a person plays professional baseball in Major League Baseball (MLB) and hits a double.
-
C.
careerTripleDoubles
Indicates that an athlete has achieved a specified number of triple-double performances over the course of their entire career.
-
D.
GrandTourDoubles
Indicates a competitive doubles tennis event or match that is part of a Grand Slam (Grand Tour) tournament series.
-
E.
grandSlamDoublesTitles
Indicates the number of Grand Slam tennis doubles titles an entity has won.
- 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_69c688109c1c8190added9a221292af0 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2303c6881909405f0d6089dbe12 |
completed | March 27, 2026, 6:53 p.m. |
| PD | Predicate disambiguation | batch_69c6d094105881909c5806eb4afa6306 |
completed | March 27, 2026, 6:46 p.m. |
| PDg | Predicate description generation | batch_69c6d1d5f1908190989efc8a2d18c965 |
completed | March 27, 2026, 6:52 p.m. |
Created at: March 27, 2026, 2:12 p.m.