Triple
T36639726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EU civil service |
E904549
|
entity |
| Predicate | employsPeopleFrom |
P36133
|
FINISHED |
| Object | European Union member states |
—
|
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: European Union member states | Statement: [EU civil service, employsPeopleFrom, European Union member states]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employsPeopleFrom Context triple: [EU civil service, employsPeopleFrom, European Union member states]
-
A.
employedPeople
Indicates that there exists a relationship where people are currently working in jobs or positions, typically under an employer.
-
B.
employsOrEmployed
Indicates that one entity currently employs or previously employed another entity in a work or service relationship.
-
C.
employedApproximately
Indicates that one entity employs another in a manner where the number, duration, or extent of employment is approximate rather than exact.
-
D.
hasEmployees
chosen
Indicates that one entity employs one or more other entities as its workers or staff.
-
E.
employsApproximateNumberOfPeople
Indicates that an entity employs a roughly estimated or approximate number of people, rather than an exact headcount.
- 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_69f76e6c63e48190b1d0c3a79a6c7406 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fcc4b700748190ae00b21d09c96695 |
completed | May 7, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69fcb0f9d3d881908a049475182fb039 |
completed | May 7, 2026, 3:34 p.m. |
Created at: May 3, 2026, 4:11 p.m.