Triple
T34510480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CA 85 |
E886003
|
entity |
| Predicate | lanesUse |
P104384
|
FINISHED |
| Object | HOV lanes during peak hours |
—
|
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: HOV lanes during peak hours | Statement: [CA 85, lanesUse, HOV lanes during peak hours]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lanesUse Context triple: [CA 85, lanesUse, HOV lanes during peak hours]
-
A.
hasLanes
Indicates that an entity, such as a road or pathway, is divided into one or more distinct lanes for traffic or movement.
-
B.
laneCount
Indicates the number of parallel lanes associated with a given road or roadway segment.
-
C.
typicalLanes
Indicates the usual or standard number or configuration of lanes associated with a road or similar transportation segment.
-
D.
hasCarpoolLanes
chosen
Indicates that a road, route, or transportation facility includes designated carpool (high-occupancy vehicle) lanes available for use.
-
E.
hasDedicatedLanes
Indicates that specific lanes within a route or roadway are reserved exclusively for a particular type of traffic or use.
- 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_69f349cc0220819081f154c6964f4dc2 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71fb1ab3881908e2f7c0e6f23db49 |
completed | May 3, 2026, 10:13 a.m. |
| PD | Predicate disambiguation | batch_69f71cc6397881909aaad37a9daa8a7e |
completed | May 3, 2026, 10 a.m. |
Created at: May 1, 2026, 2:01 a.m.