Triple
T12946220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zoe Barnes |
E309772
|
entity |
| Predicate | professionField |
P24248
|
FINISHED |
| Object | political journalism |
—
|
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: political journalism | Statement: [Zoe Barnes, professionField, political journalism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionField Context triple: [Zoe Barnes, professionField, political journalism]
-
A.
professionalCategory
Indicates the classification of an entity according to its professional field, role, or occupational domain.
-
B.
careerField
chosen
Indicates the professional domain or occupational area in which an entity works or specializes.
-
C.
professionalSector
Indicates the industry or field in which an entity conducts its professional or occupational activities.
-
D.
leftProfession
Indicates that an entity has stopped or abandoned a particular profession or occupation they previously held.
-
E.
careerType
Indicates the kind or category of professional occupation or career path associated with an 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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69d97db69f548190a1a693bc0d6c191a |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 5:43 p.m.