Triple
T15919702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Football Conference |
E386059
|
entity |
| Predicate | professionalStatusTrend |
P120535
|
FINISHED |
| Object | increasingly professional over time |
—
|
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: increasingly professional over time | Statement: [Football Conference, professionalStatusTrend, increasingly professional over time]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionalStatusTrend Context triple: [Football Conference, professionalStatusTrend, increasingly professional over time]
-
A.
businessCareer
Indicates a relationship where an entity’s professional life, roles, or progression is specifically within the field of business or commerce.
-
B.
professionalSector
Indicates the industry or field in which an entity conducts its professional or occupational activities.
-
C.
professionalCategory
Indicates the classification of an entity according to its professional field, role, or occupational domain.
-
D.
peakEmployment
Indicates that an entity has reached its highest level of employment or workforce size during a specified period.
-
E.
occupationAspiration
Indicates a person's desired or intended future occupation or career goal.
- 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_69d86da686e4819097cbf3b1fc2d881d |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e172b48b308190bc430b2308cbc75b |
completed | April 16, 2026, 11:37 p.m. |
| PD | Predicate disambiguation | batch_69e142cf5c548190a931f7b58144cd31 |
completed | April 16, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69e172b213e481909ee0c05e16229a26 |
completed | April 16, 2026, 11:37 p.m. |
Created at: April 10, 2026, 4:52 a.m.