Triple
T26389082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metropolitans 92 |
E663361
|
entity |
| Predicate | departmentNumberMeaning |
P145061
|
FINISHED |
| Object | Hauts-de-Seine department code |
—
|
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: Hauts-de-Seine department code | Statement: [Metropolitans 92, departmentNumberMeaning, Hauts-de-Seine department code]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: departmentNumberMeaning Context triple: [Metropolitans 92, departmentNumberMeaning, Hauts-de-Seine department code]
-
A.
departmentNumber
Indicates the specific numeric code assigned to identify a particular department within an organization or system.
-
B.
codeNumberMeaning
chosen
Indicates that a specific code number is associated with and represents a particular meaning or interpretation.
-
C.
departmentType
Indicates the classification or category of a department, specifying what kind of department it is.
-
D.
department
Indicates that one entity functions as an organizational unit or division within another, typically larger, entity.
-
E.
officeNumber
Indicates the specific room or suite number assigned to an office within a building or complex.
- 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_69f60b89cc048190a9feb24466006be0 |
completed | May 2, 2026, 2:34 p.m. |
Created at: April 26, 2026, 11:24 p.m.