Triple
T21841049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Knight Ridder |
E539252
|
entity |
| Predicate | dateNumberOfEmployees |
P63085
|
FINISHED |
| Object | around 2005 |
—
|
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: around 2005 | Statement: [Knight Ridder, dateNumberOfEmployees, around 2005]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dateNumberOfEmployees Context triple: [Knight Ridder, dateNumberOfEmployees, around 2005]
-
A.
employerNumberCharacteristic
Indicates a relationship where an entity is associated with a specific numeric characteristic that quantifies or identifies an employer.
-
B.
hasNumberOfCompanies
Indicates the quantitative relationship specifying how many companies are associated with a given entity.
-
C.
typicalCompanySize
Indicates the usual or most common number of employees associated with a company.
-
D.
hasEmployees
Indicates that one entity employs one or more other entities as its workers or staff.
-
E.
numberOfEmployeesDate
chosen
Indicates the specific date on which the recorded number of employees for an entity is valid or measured.
- 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_69e0c476c3c88190a92d08ebb59a128a |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f0a7ab71e081908e3d3293743e6409 |
completed | April 28, 2026, 12:27 p.m. |
| PD | Predicate disambiguation | batch_69e6be8c14748190bdcc44a14d50bea4 |
completed | April 21, 2026, 12:02 a.m. |
Created at: April 16, 2026, 6:55 p.m.