Triple
T469129
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Appellate Division of the Supreme Court of New York |
E8514
|
entity |
| Predicate | numberOfDepartments |
P1679
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Appellate Division of the Supreme Court of New York, numberOfDepartments, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfDepartments Context triple: [Appellate Division of the Supreme Court of New York, numberOfDepartments, 4]
-
A.
numberOfOffices
Indicates the total count of offices associated with a given entity.
-
B.
departmentNumber
Indicates the specific numeric code assigned to identify a particular department within an organization or system.
-
C.
numberOfCountryOffices
Indicates the total count of offices or branches that an organization maintains across different countries.
-
D.
numberOfDistricts
chosen
Indicates the total count of districts associated with a given entity or area.
-
E.
departmentType
Indicates the classification or category of a department, specifying what kind of department it is.
- 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_69a2e7f3aeb48190a19453e3a043f486 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2efee0ea0819099d87f3727c03bc7 |
completed | Feb. 28, 2026, 1:38 p.m. |
| PD | Predicate disambiguation | batch_69a2edebb3988190907992a584b4e260 |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.