Triple
T15137310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leipziger Straße intersection |
E361586
|
entity |
| Predicate | roadTypeOnOneAxis |
P1019
|
FINISHED |
| Object | urban arterial road |
—
|
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: urban arterial road | Statement: [Leipziger Straße intersection, roadTypeOnOneAxis, urban arterial road]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roadTypeOnOneAxis Context triple: [Leipziger Straße intersection, roadTypeOnOneAxis, urban arterial road]
-
A.
roadType
chosen
Indicates the classification or category of a road based on its functional or physical characteristics.
-
B.
roadFeature
Indicates that an entity is a specific physical or functional characteristic associated with a road, such as its structure, markings, or related infrastructure.
-
C.
roadPassType
Indicates the type or category of permission or authorization required to use or pass along a particular road or route.
-
D.
roadSystem
Indicates a relationship where multiple roads are organized and connected as part of a larger, integrated transportation network or infrastructure.
-
E.
roadName
Indicates the specific name assigned to a road that identifies it within a transportation or address system.
- 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_69d85a06450081909c5a14ea9851a15e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e005b59b488190b0016970647e7483 |
completed | April 15, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69deb9713fe881909dec2fd3f6c84b39 |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:07 a.m.