Triple
T20301472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alexion Pharmaceuticals |
E505487
|
entity |
| Predicate | hasEmployeeCountRange |
P91521
|
FINISHED |
| Object | >= 3000 employees (approximate) |
—
|
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: >= 3000 employees (approximate) | Statement: [Alexion Pharmaceuticals, hasEmployeeCountRange, >= 3000 employees (approximate)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEmployeeCountRange Context triple: [Alexion Pharmaceuticals, hasEmployeeCountRange, >= 3000 employees (approximate)]
-
A.
hasEmployeeRange
chosen
Indicates the range or limits on the number of employees associated with an entity.
-
B.
hasEmployees
Indicates that one entity employs one or more other entities as its workers or staff.
-
C.
appliesToEmployerSize
Indicates that something (such as a rule, policy, or condition) is applicable only to employers of a specified size or within a defined employer size range.
-
D.
employsApproximateNumberOfPeople
Indicates that an entity employs a roughly estimated or approximate number of people, rather than an exact headcount.
-
E.
typicalCompanySize
Indicates the usual or most common number of employees associated with a company.
- 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_69e0b4b8ab648190906e18538c250148 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6770d82b48190b21ce7c52ec6d5a0 |
completed | April 20, 2026, 6:57 p.m. |
| PD | Predicate disambiguation | batch_69e55b21b09081909e46691b6f45a07f |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 16, 2026, 11:17 a.m.