Triple
T706314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mariano Rivera |
E14106
|
entity |
| Predicate | careerSaves |
P18723
|
FINISHED |
| Object | 652 |
—
|
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: 652 | Statement: [Mariano Rivera, careerSaves, 652]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerSaves Context triple: [Mariano Rivera, careerSaves, 652]
-
A.
careerStart
Indicates the point in time when an entity begins its professional career or main occupational activity.
-
B.
careerImpact
Indicates how one entity influences or changes another entity’s professional trajectory, opportunities, or outcomes.
-
C.
careerOPS
Indicates a relationship where an entity’s career on-base plus slugging (OPS) statistic is recorded or associated with that entity.
-
D.
workIncludes
Indicates that a work (such as a project, document, or creative piece) contains or incorporates another specified component, part, or element.
-
E.
careerHits
Indicates the total number of hits a player has accumulated over the course of their entire professional 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_69a493494ec48190ae6751683625a9ba |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a58d4c3c8190ad4527d14bca5e6e |
completed | March 1, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69a4a4edc33881909a978268f6dd5d82 |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a58c0a84819094f07658dc651b36 |
completed | March 1, 2026, 8:46 p.m. |
Created at: March 1, 2026, 7:36 p.m.