Triple
T25129507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pipera (Bucharest neighborhood) |
E629486
|
entity |
| Predicate | typicalCompanies |
P119579
|
FINISHED |
| Object | multinational corporations |
—
|
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: multinational corporations | Statement: [Pipera (Bucharest neighborhood), typicalCompanies, multinational corporations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCompanies Context triple: [Pipera (Bucharest neighborhood), typicalCompanies, multinational corporations]
-
A.
typicalEnterprises
chosen
Indicates that an entity is a standard or representative example of enterprises within a given context or category.
-
B.
typicalEmployer
Indicates that one entity is the kind of organization or person that commonly or usually employs the other entity.
-
C.
targetCompanies
Indicates that certain companies are the intended focus or recipients of a particular action, effort, or objective.
-
D.
typicalEmployerBrand
Indicates that an entity is the employer brand that is most commonly or characteristically associated with another entity.
-
E.
typicalEmployerUnit
Indicates that one entity is the standard or characteristic organizational unit that employs or is expected to employ 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_69e2ff3288048190bd82c3b7f7bd0e62 |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f7979a073881909a4fde2558e6b6f3 |
completed | May 3, 2026, 6:44 p.m. |
| PD | Predicate disambiguation | batch_69f7961550f88190b7bb8a9155458b54 |
completed | May 3, 2026, 6:38 p.m. |
Created at: April 18, 2026, 6:28 a.m.