Triple
T36577677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richard D. Sears |
E902300
|
entity |
| Predicate | numberOfDoublesTitlesAtUSNationalChampionships |
P204947
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [Richard D. Sears, numberOfDoublesTitlesAtUSNationalChampionships, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfDoublesTitlesAtUSNationalChampionships Context triple: [Richard D. Sears, numberOfDoublesTitlesAtUSNationalChampionships, 6]
-
A.
numberOfNationalTitles
Indicates the total count of national-level championship titles that an entity has won.
-
B.
numberOfUSOpenChampionshipsWon
Indicates the count of US Open Championship titles that an entity has won.
-
C.
U.S. National Championships singles titles
Indicates the number of U.S. National Championships singles titles an entity has won.
-
D.
grandSlamDoublesTitles
Indicates the number of Grand Slam tennis doubles titles an entity has won.
-
E.
numberOfOpenChampionshipsWon
Indicates the total count of Open Championships that 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_69f76e64d8908190868473959a250b94 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0bf4b88190bdcfae9a14b51f0a |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82f8c88190bd77a086023ac0e1 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:11 p.m.