Triple
T18670192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SR 303 |
E456453
|
entity |
| Predicate | connectsWith |
P37
|
FINISHED |
| Object | Loop 202 |
—
|
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: Loop 202 | Statement: [SR 303, connectsWith, Loop 202]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Loop 202 Context triple: [SR 303, connectsWith, Loop 202]
-
A.
Loop 202
chosen
Loop 202 is a major freeway in the Phoenix, Arizona metropolitan area that forms part of the region’s beltway system, helping route traffic around the city.
-
B.
Loop 286
Loop 286 is a state highway loop that serves as a beltway around the city of Paris in Lamar County, Texas.
-
C.
Loop 820
Loop 820 is a major beltway highway encircling much of the Fort Worth, Texas metropolitan area, serving as a key route for regional traffic and suburban access.
-
D.
Loop 1604
Loop 1604 is a major highway encircling much of San Antonio, Texas, serving as a key route for regional traffic and access to suburban districts and commercial areas.
-
E.
Loop 101
Loop 101 is a major freeway encircling much of the Phoenix metropolitan area, connecting numerous suburbs and serving as a key route for regional traffic.
- 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_69d8d38f72b4819090a935175d9ca8af |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e556b1140c81908002c27a33c03ae2 |
completed | April 19, 2026, 10:26 p.m. |
Created at: April 10, 2026, 11:48 a.m.