Triple
T5381253
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DED |
E113087
|
entity |
| Predicate | professionalArea |
P24248
|
FINISHED |
| Object | engineering |
—
|
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: engineering | Statement: [DED, professionalArea, engineering]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionalArea Context triple: [DED, professionalArea, engineering]
-
A.
professionalSector
Indicates the industry or field in which an entity conducts its professional or occupational activities.
-
B.
professionalScope
Indicates the range of activities, responsibilities, or roles that fall within a person’s or organization’s recognized professional duties or expertise.
-
C.
professional
Indicates that one entity has a formal, occupation-related role, service, or expertise in relation to another entity.
-
D.
professionalSince
Indicates the point in time when an entity began its professional activity or career in a given role or field.
-
E.
careerField
chosen
Indicates the professional domain or occupational area in which an entity works or specializes.
- 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_69bd4436a1988190af18dcff7fd306b4 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd88801b188190b9ac35ed89167fa3 |
completed | March 20, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69bd846172788190969f24bc7503c05e |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:03 p.m.