Triple
T30793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | El Camino Real |
E613
|
entity |
| Predicate | hasLanes |
P2128
|
FINISHED |
| Object | multiple lanes in each direction in many segments |
—
|
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: multiple lanes in each direction in many segments | Statement: [El Camino Real, hasLanes, multiple lanes in each direction in many segments]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanes Context triple: [El Camino Real, hasLanes, multiple lanes in each direction in many segments]
-
A.
hasMajorHighway
Indicates that a location or area is served by or directly connected to a major highway route.
-
B.
hasNumberOfDivisions
Indicates the relationship that specifies how many divisions or subunits an entity possesses.
-
C.
hasJunctionWith
Indicates that one entity meets or intersects with another at a shared junction point.
-
D.
hasStreet
Indicates that an entity is located on, associated with, or identified by a particular street.
-
E.
numberOfStripes
Indicates the count of distinct stripe markings associated with an entity.
- F. None of above. chosen
Provenance (4 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_69a2479dec388190967ba648663442c9 |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a2490d80a0819083bf604c1229e903 |
completed | Feb. 28, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69a2486eb01881909241540dda28e1ff |
completed | Feb. 28, 2026, 1:44 a.m. |
| PDg | Predicate description generation | batch_69a2490c9c348190bf8536a08415b94a |
completed | Feb. 28, 2026, 1:46 a.m. |
Created at: Feb. 28, 2026, 1:44 a.m.