Triple
T22285970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Virginia State Route 134 |
E550861
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | SR 134 |
—
|
NE NERFINISHED |
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: SR 134 | Statement: [Virginia State Route 134, abbreviation, SR 134]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SR 134 Context triple: [Virginia State Route 134, abbreviation, SR 134]
-
A.
SR 134
chosen
SR 134 is a state highway designation used for specific numbered routes within a U.S. state's road network.
-
B.
SR 133
SR 133 is a state highway in Maine that connects several central Maine communities and serves as a regional north–south transportation route.
-
C.
SR 132
SR 132 is a state highway in Maine that connects several small communities and links with other major regional routes.
-
D.
SR 132
SR 132 is a state highway in California that runs east–west through the Central Valley, connecting the city of Modesto to surrounding rural and regional routes.
-
E.
SR 131
SR 131 is a state highway in Maine that runs through Knox and Waldo counties, connecting several coastal and inland communities.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e44d538819097c6b8f333af3352 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15607d9948190b4b8e9cd7fa4d390 |
completed | April 29, 2026, 12:51 a.m. |
Created at: April 16, 2026, 8:40 p.m.