Triple
T5340281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arthur Andersen |
E123927
|
entity |
| Predicate | peakEmployeeCount |
P3346
|
FINISHED |
| Object | about 85,000 |
—
|
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: about 85,000 | Statement: [Arthur Andersen, peakEmployeeCount, about 85,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peakEmployeeCount Context triple: [Arthur Andersen, peakEmployeeCount, about 85,000]
-
A.
employsApproximateNumberOfPeople
Indicates that an entity employs a roughly estimated or approximate number of people, rather than an exact headcount.
-
B.
staffSize
Indicates the number of staff members associated with an entity.
-
C.
hasEmployees
Indicates that one entity employs one or more other entities as its workers or staff.
-
D.
employerNumberCharacteristic
Indicates a relationship where an entity is associated with a specific numeric characteristic that quantifies or identifies an employer.
-
E.
peakPersonnel
chosen
Indicates the maximum number of personnel involved or present at any point during a specified period or activity.
- 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_69bd464b07f8819095aa76577c9829e4 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd85e86edc81908d87933db6489f91 |
completed | March 20, 2026, 5:37 p.m. |
| PD | Predicate disambiguation | batch_69bd845a62b081909782863865b257a9 |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2 p.m.