Triple
T17282752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stephen Baxter |
E419572
|
entity |
| Predicate | typeOfManager |
P84552
|
FINISHED |
| Object | club football manager |
—
|
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: club football manager | Statement: [Stephen Baxter, typeOfManager, club football manager]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfManager Context triple: [Stephen Baxter, typeOfManager, club football manager]
-
A.
managerType
chosen
Indicates the specific category or role classification of a manager within an organizational or system context.
-
B.
typeOfRole
Indicates that one entity specifies the kind or category of role that another entity holds or performs.
-
C.
manager
Indicates that one entity holds a supervisory or administrative role with authority and responsibility over another entity.
-
D.
hasTypeOfManagement
Indicates that an entity is associated with or operates under a specified form or style of management.
-
E.
personnelType
Indicates the classification or role category assigned to a person within an organization or system.
- F. None of above.
Provenance (3 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_69d886da626481908a14ce7830329a35 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e4332b19f481908acfa88b2f57c5dc |
completed | April 19, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69e3832c3b98819091967ac7e91ba316 |
completed | April 18, 2026, 1:12 p.m. |
Created at: April 10, 2026, 5:40 a.m.