Triple
T1148646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Connie Mack |
E23624
|
entity |
| Predicate | totalSeasonsManaged |
P26065
|
FINISHED |
| Object | 50 |
—
|
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: 50 | Statement: [Connie Mack, totalSeasonsManaged, 50]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalSeasonsManaged Context triple: [Connie Mack, totalSeasonsManaged, 50]
-
A.
numberOfSeasonsWithTeam
Indicates the total count of seasons an entity (e.g., a player or coach) has spent with a particular team.
-
B.
pennantsWonAsManager
Indicates the number of league pennants a person has won in their role as a team manager.
-
C.
wonAsManager
Indicates that one entity achieved a victory or title while serving in the role of manager of the other entity.
-
D.
consecutiveWinningSeasons
Indicates that an entity (such as a team or individual) has achieved winning seasons in back-to-back or uninterrupted consecutive years.
-
E.
headCoachTenure
Indicates the duration or period during which a specific individual serves as the head coach of a team or organization.
- 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_69a493f0d32c8190ac74bad3c87f2641 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bd0bed00819091d71983d787a030 |
completed | March 1, 2026, 10:26 p.m. |
| PD | Predicate disambiguation | batch_69a4bb4ee3988190ac89c5ae5b10e316 |
completed | March 1, 2026, 10:18 p.m. |
| PDg | Predicate description generation | batch_69a4bd0ab5f88190bb583fc63b4cc150 |
completed | March 1, 2026, 10:26 p.m. |
Created at: March 1, 2026, 7:44 p.m.