Triple
T2496912
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jack Charlton |
E52171
|
entity |
| Predicate | yearsActiveAsManager |
P15414
|
FINISHED |
| Object | 1973–1996 |
—
|
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: 1973–1996 | Statement: [Jack Charlton, yearsActiveAsManager, 1973–1996]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: yearsActiveAsManager Context triple: [Jack Charlton, yearsActiveAsManager, 1973–1996]
-
A.
yearsActiveAsCoach
chosen
Indicates the span of time, typically in years, during which an individual has served in a coaching role.
-
B.
activeInYears
Indicates that an entity was active or operational during the specified years or year range.
-
C.
totalSeasonsManaged
Indicates the total number of seasons during which an entity has managed another entity (such as a team, organization, or project).
-
D.
activeYearsInCareer
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
E.
teamTenure
Indicates the duration or length of time an entity has been part of a particular team.
- 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_69ab4955111c8190835bf619adec21ff |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1ad2f8c81908853e97d75081e84 |
completed | March 7, 2026, 7:20 a.m. |
| PD | Predicate disambiguation | batch_69abd0b980b481908d4932bcea4a6167 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:46 p.m.