Triple
T33988665
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boehringer Ingelheim |
E871479
|
entity |
| Predicate | hasNumberOfEmployeesOver |
P91521
|
FINISHED |
| Object | 50000 |
—
|
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: 50000 | Statement: [Boehringer Ingelheim, hasNumberOfEmployeesOver, 50000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfEmployeesOver Context triple: [Boehringer Ingelheim, hasNumberOfEmployeesOver, 50000]
-
A.
hasEmployees
Indicates that one entity employs one or more other entities as its workers or staff.
-
B.
hasNumberOfCompanies
Indicates the quantitative relationship specifying how many companies are associated with a given entity.
-
C.
isOneOfLargestEmployers
Indicates that an entity ranks among the largest organizations in terms of the number of people it employs.
-
D.
employsApproximateNumberOfPeople
Indicates that an entity employs a roughly estimated or approximate number of people, rather than an exact headcount.
-
E.
hasEmployeeRange
chosen
Indicates the range or limits on the number of employees associated with an 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_69f3499e964c8190b674b03f6f791b4b |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fe8f74748c8190bd14a856c057f9f7 |
completed | May 9, 2026, 1:35 a.m. |
| PD | Predicate disambiguation | batch_69fe8e7ed8088190929e0df67aca4de9 |
completed | May 9, 2026, 1:31 a.m. |
Created at: May 1, 2026, 1:50 a.m.