Triple
T181636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Georgia State Route System |
E3888
|
entity |
| Predicate | hasNumberingScheme |
P3378
|
FINISHED |
| Object | sequential route numbers |
—
|
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: sequential route numbers | Statement: [Georgia State Route System, hasNumberingScheme, sequential route numbers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberingScheme Context triple: [Georgia State Route System, hasNumberingScheme, sequential route numbers]
-
A.
numberingType
chosen
Indicates the scheme or style used to assign sequential numbers or labels within an ordered set.
-
B.
hasNumberCategory
Indicates that an entity is associated with a specific numerical classification or type.
-
C.
hasSectionCount
Indicates that an entity is associated with a specific number of sections it contains or comprises.
-
D.
hasNumberOfDivisions
Indicates the relationship that specifies how many divisions or subunits an entity possesses.
-
E.
hasStandardLetterCount
Indicates that an entity’s associated text or label contains a number of letters that matches a predefined standard or expected count.
- 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_69a25497e2f08190a040f8c6e1842643 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25923507c8190bd7f6eda404b0da0 |
completed | Feb. 28, 2026, 2:55 a.m. |
| PD | Predicate disambiguation | batch_69a2566ccc288190add5624ede96d82b |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:40 a.m.