Triple
T27480479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FR-90 |
E693582
|
entity |
| Predicate | subdivisionNumericCode |
P22016
|
FINISHED |
| Object | 90 |
—
|
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: 90 | Statement: [FR-90, subdivisionNumericCode, 90]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subdivisionNumericCode Context triple: [FR-90, subdivisionNumericCode, 90]
-
A.
usesNumericSubdivisionCode
Indicates that one entity employs a numeric subdivision code system to identify or classify parts or regions of another entity.
-
B.
hasSubdivisionCode
chosen
Indicates that an entity is associated with a specific code identifying one of its internal subdivisions (such as a state, province, or region).
-
C.
subdivisionCodeFormat
Indicates the specific pattern or structure used to represent codes assigned to administrative or geographic subdivisions.
-
D.
hasSubdivisionCodePart
Indicates that an entity’s subdivision code includes or is composed of the referenced code segment or component.
-
E.
subdivisionCodeLength
Indicates the length (number of characters or digits) used in the code that identifies a particular subdivision within a larger entity or system.
- 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_69ef5381f2648190a2392d0fab833095 |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69f65a6c900881908f18b61273d7bf8d |
completed | May 2, 2026, 8:11 p.m. |
| PD | Predicate disambiguation | batch_69f659ce58408190ba9e007b4810d4d0 |
completed | May 2, 2026, 8:08 p.m. |
Created at: April 27, 2026, 12:59 p.m.