Triple
T7893538
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LinkedIn headquarters |
E183293
|
entity |
| Predicate | numberOfEmployeesOnSite |
P17907
|
FINISHED |
| Object | thousands |
—
|
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: thousands | Statement: [LinkedIn headquarters, numberOfEmployeesOnSite, thousands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfEmployeesOnSite Context triple: [LinkedIn headquarters, numberOfEmployeesOnSite, thousands]
-
A.
employsApproximateNumberOfPeople
chosen
Indicates that an entity employs a roughly estimated or approximate number of people, rather than an exact headcount.
-
B.
numberOfEmployeesDate
Indicates the specific date on which the recorded number of employees for an entity is valid or measured.
-
C.
userCount
Indicates the number of users associated with or involved in a given context or entity.
-
D.
hasEmployees
Indicates that one entity employs one or more other entities as its workers or staff.
-
E.
estimatedMemberCount
Indicates the approximate or predicted number of members 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_69ca828c474c8190a254d6499871eaff |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a008fb88190a039fec40483ab93 |
completed | March 31, 2026, 3:05 a.m. |
| PD | Predicate disambiguation | batch_69cae92d94448190b4425bbfb64c658c |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:01 p.m.