Triple
T26389081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metropolitans 92 |
E663361
|
entity |
| Predicate | departmentNumberReference |
P9698
|
FINISHED |
| Object | 92 |
—
|
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: 92 | Statement: [Metropolitans 92, departmentNumberReference, 92]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: departmentNumberReference Context triple: [Metropolitans 92, departmentNumberReference, 92]
-
A.
departmentNumber
chosen
Indicates the specific numeric code assigned to identify a particular department within an organization or system.
-
B.
department
Indicates that one entity functions as an organizational unit or division within another, typically larger, entity.
-
C.
laterDepartment
Indicates that one department occurs or is considered after another in a defined ordering or sequence.
-
D.
propDepartment
Indicates that one entity functions as a department or organizational subdivision associated with another entity.
-
E.
basedInDepartment
Indicates that an entity operates or has its primary affiliation within a specific department.
- 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_69ee88374adc81909868f3bab374a32f |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f610be3e848190b7acb7675e37e1f5 |
completed | May 2, 2026, 2:57 p.m. |
| PD | Predicate disambiguation | batch_69f602d2ec748190ae95154f34c7878f |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 26, 2026, 11:24 p.m.