Triple
T32316427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alexey Miller |
E825643
|
entity |
| Predicate | hasEmployerCharacteristic |
P138237
|
FINISHED |
| Object | state-controlled company |
—
|
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: state-controlled company | Statement: [Alexey Miller, hasEmployerCharacteristic, state-controlled company]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEmployerCharacteristic Context triple: [Alexey Miller, hasEmployerCharacteristic, state-controlled company]
-
A.
employerNumberCharacteristic
Indicates a relationship where an entity is associated with a specific numeric characteristic that quantifies or identifies an employer.
-
B.
hasIndustrialEmployer
Indicates that an entity is employed by, or has an employment relationship with, an industrial organization or company.
-
C.
employmentCharacteristic
chosen
Indicates a specific attribute, condition, or quality associated with a person’s employment or job situation.
-
D.
employerStatus
Indicates the current employment relationship or condition between an employer and a worker, such as whether the person is actively employed, terminated, retired, or on leave.
-
E.
namedForEmployer
Indicates that an entity is named after, or in honor of, its employer.
- 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_69f3491213b88190a57094d8697a7455 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6bdbb00d4819083dd799d7d417f20 |
completed | May 3, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69f6b632cf788190a3d0c08cd026b84b |
completed | May 3, 2026, 2:42 a.m. |
Created at: May 1, 2026, 12:46 a.m.