Triple
T222101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deacon Phillippe |
E4235
|
entity |
| Predicate | careerWins |
P8292
|
FINISHED |
| Object | 189 |
—
|
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: 189 | Statement: [Deacon Phillippe, careerWins, 189]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerWins Context triple: [Deacon Phillippe, careerWins, 189]
-
A.
careerHits
Indicates the total number of hits a player has accumulated over the course of their entire professional career.
-
B.
winnerPoints
Indicates the number of points earned by the winning participant or entity in a competition or event.
-
C.
mostOverallWinsRecord
Indicates that the subject holds the record for having the greatest total number of wins compared to all others in the relevant context.
-
D.
careerDoubles
Indicates the total number of doubles a person has achieved over the course of their entire career.
-
E.
careerRunsScored
Indicates the total number of runs an entity has scored over the entire duration of their playing career.
- 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_69a2573508588190b522c2476d91acfe |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25c705fd88190bfee7f5e1f7cee17 |
completed | Feb. 28, 2026, 3:09 a.m. |
| PD | Predicate disambiguation | batch_69a25b5617788190814358aee3f7ae37 |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25c2bda788190bcfc0bc94686f9e0 |
completed | Feb. 28, 2026, 3:08 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.