Triple
T28812559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grace Coddington |
E727551
|
entity |
| Predicate | careerStartAsModel |
P112203
|
FINISHED |
| Object | teenager |
—
|
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: teenager | Statement: [Grace Coddington, careerStartAsModel, teenager]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerStartAsModel Context triple: [Grace Coddington, careerStartAsModel, teenager]
-
A.
careerStartAs
chosen
Indicates the role, position, or occupation in which an individual first began their professional career.
-
B.
careerStart
Indicates the point in time when an entity begins its professional career or main occupational activity.
-
C.
careerType
Indicates the kind or category of professional occupation or career path associated with an entity.
-
D.
managedCareerOf
Indicates that one entity was responsible for overseeing, directing, or handling the professional career of another entity.
-
E.
targetCareer
Indicates that one entity is the intended or pursued career or professional goal of another entity.
- 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_69f0319c38948190bca746ad60fd25ba |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69f658f05db88190b613173eece383d4 |
completed | May 2, 2026, 8:05 p.m. |
| PD | Predicate disambiguation | batch_69f65760fd3081908ffe014a5e2bf069 |
completed | May 2, 2026, 7:58 p.m. |
Created at: April 28, 2026, 6:31 a.m.