Triple
T365477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Medtronic |
E7950
|
entity |
| Predicate | numberOfEmployeesApproximate |
P803
|
FINISHED |
| Object | 95000 |
—
|
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: 95000 | Statement: [Medtronic, numberOfEmployeesApproximate, 95000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfEmployeesApproximate Context triple: [Medtronic, numberOfEmployeesApproximate, 95000]
-
A.
employedApproximately
chosen
Indicates that one entity employs another in a manner where the number, duration, or extent of employment is approximate rather than exact.
-
B.
employedPeople
Indicates that there exists a relationship where people are currently working in jobs or positions, typically under an employer.
-
C.
hasPopulationApproximate
Indicates that an entity has an estimated or approximate population size, rather than an exact count.
-
D.
passengersCountApproximate
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
-
E.
numberOfBoardMembers
Indicates the total count of individuals who serve as members on a board.
- 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_69a2e7e880008190a6ad7e06e5d03007 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ebe7d4d0819083daeb7686ae1914 |
completed | Feb. 28, 2026, 1:21 p.m. |
| PD | Predicate disambiguation | batch_69a2e95dbb208190b277fc5352a4ee84 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.