Triple
T856220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tony La Russa |
E18496
|
entity |
| Predicate | careerManagerialWins |
P20661
|
FINISHED |
| Object | 2728 |
—
|
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: 2728 | Statement: [Tony La Russa, careerManagerialWins, 2728]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerManagerialWins Context triple: [Tony La Russa, careerManagerialWins, 2728]
-
A.
careerWins
Indicates the total number of wins an individual or entity has accumulated over the course of their entire career.
-
B.
careerWinLossRecord
Indicates the overall tally of wins and losses an entity has accumulated over the entire span of its career.
-
C.
careerHits
Indicates the total number of hits a player has accumulated over the course of their entire professional career.
-
D.
careerLosses
Indicates the total number of defeats or losses an entity has accumulated over the course of its entire career.
-
E.
careerStrikeouts
Indicates the total number of batters a pitcher has struck out 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_69a4938bdd3c8190a954a3c11844d9cf |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac3c172481908ed164ee1579ec28 |
completed | March 1, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69a4aa834a588190bca4a0eb83fb3eb6 |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4ab4893e481908632102d240466dc |
completed | March 1, 2026, 9:10 p.m. |
Created at: March 1, 2026, 7:39 p.m.