Triple
T6611703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martina Navratilova |
E149251
|
entity |
| Predicate | grandSlamTitlesTotal |
P9284
|
FINISHED |
| Object | 59 |
—
|
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: 59 | Statement: [Martina Navratilova, grandSlamTitlesTotal, 59]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grandSlamTitlesTotal Context triple: [Martina Navratilova, grandSlamTitlesTotal, 59]
-
A.
grandSlamSinglesTitles
chosen
Indicates the number of Grand Slam singles tennis titles an entity has won.
-
B.
GrandSlams
Indicates that an entity has won or is associated with victories in major Grand Slam tournaments within a given sport.
-
C.
grandSlamOrder
Indicates that one event or achievement in a sequence is a grand slam and specifies its relative order or position among other such events.
-
D.
otherGrandSlams
Indicates that an entity is associated with Grand Slam tournaments other than a primary or specifically referenced one.
-
E.
numberOfUSOpenChampionshipsWon
Indicates the count of US Open Championship titles that an entity has won.
- 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_69c687ebc680819094caf71faba2efe2 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6cf3796d08190a26e988386089447 |
completed | March 27, 2026, 6:40 p.m. |
| PD | Predicate disambiguation | batch_69c6acfed25481909cac74c84a9fe088 |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:57 p.m.