Triple
T3147927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Slam tennis tournaments |
E65806
|
entity |
| Predicate | rankingSignificance |
P16379
|
FINISHED |
| Object | highest ranking points in tennis |
—
|
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: highest ranking points in tennis | Statement: [Grand Slam tennis tournaments, rankingSignificance, highest ranking points in tennis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingSignificance Context triple: [Grand Slam tennis tournaments, rankingSignificance, highest ranking points in tennis]
-
A.
rankSignificance
chosen
Indicates how important or influential one entity is relative to others within a specified context or ordering.
-
B.
rankingImpact
Indicates how an entity’s position or level in a ranking is affected or influenced by another factor or action.
-
C.
rankingType
Indicates the specific basis or method by which items are ordered or ranked relative to one another.
-
D.
rankEquivalent
Indicates that two entities hold the same rank or hierarchical level within a given ordering or classification system.
-
E.
rankingPoints
Indicates the number of points assigned to an entity based on its position or performance in a ranking or competition.
- 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_69ad8584485081909ed529e890cadc4a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada59a54188190a2e020fd4004d734 |
completed | March 8, 2026, 4:36 p.m. |
| PD | Predicate disambiguation | batch_69ad9dfa3d9081908425aa636bb9b897 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:05 p.m.