Triple
T2740848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bobby Doerr |
E60744
|
entity |
| Predicate | careerTriples |
P41295
|
FINISHED |
| Object | 89 |
—
|
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: 89 | Statement: [Bobby Doerr, careerTriples, 89]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerTriples Context triple: [Bobby Doerr, careerTriples, 89]
-
A.
careerWalks
Indicates the total number of bases on balls (walks) a player has received over the course of their entire career.
-
B.
careerAssists
Indicates the total number of assists a player has recorded over the entire span of their professional or competitive 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.
targetCareer
Indicates that one entity is the intended or pursued career or professional goal of another entity.
-
E.
careerPath
Indicates the progression or sequence of roles, positions, or occupations that an individual follows over time in their professional life.
- 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_69ab4b77febc819095603eb012cd141b |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb2f210881909126307cc92ebfef |
completed | March 7, 2026, 8 a.m. |
| PD | Predicate disambiguation | batch_69abd829f1e88190aab1d54f87c69714 |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd8dd05c48190b4f90031642c4091 |
completed | March 7, 2026, 7:50 a.m. |
Created at: March 6, 2026, 9:56 p.m.