Triple
T6679278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pete Rose |
E151935
|
entity |
| Predicate | careerSingles |
P71704
|
FINISHED |
| Object | 3215 |
—
|
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: 3215 | Statement: [Pete Rose, careerSingles, 3215]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerSingles Context triple: [Pete Rose, careerSingles, 3215]
-
A.
careerDoubles
Indicates the total number of doubles a person has achieved over the course of their entire career.
-
B.
careerSacks
Indicates the total number of times a defensive player has sacked a quarterback over the course of their entire career.
-
C.
businessCareer
Indicates a relationship where an entity’s professional life, roles, or progression is specifically within the field of business or commerce.
-
D.
careerHits
Indicates the total number of hits a player has accumulated over the course of their entire professional career.
-
E.
careerTackles
Indicates the total number of tackles a player has made over the course of their entire 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_69c687f830bc81909eb8b04dbb8450b1 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6c0aa8c5c8190a302b261f11b70cb |
completed | March 27, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c6ad0b6d00819086205b8ce30dd045 |
completed | March 27, 2026, 4:15 p.m. |
| PDg | Predicate description generation | batch_69c6c0a90a088190978061cb05dbe268 |
completed | March 27, 2026, 5:38 p.m. |
Created at: March 27, 2026, 2:03 p.m.