Triple
T500673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M74 motorway |
E10392
|
entity |
| Predicate | urbanSection |
P12103
|
FINISHED |
| Object | Glasgow southern section |
—
|
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: Glasgow southern section | Statement: [M74 motorway, urbanSection, Glasgow southern section]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: urbanSection Context triple: [M74 motorway, urbanSection, Glasgow southern section]
-
A.
urbanDevelopment
Indicates the process or activities through which urban areas are planned, expanded, or transformed, including changes to infrastructure, land use, and the built environment.
-
B.
withinUrbanArea
chosen
Indicates that one entity is located inside the spatial boundaries of an urban area associated with another entity.
-
C.
city2
Indicates a relationship where one entity is identified as a city associated with, located in, or otherwise linked to another entity.
-
D.
urbanAreaType
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
-
E.
cityPanorama
Indicates a wide, comprehensive visual view or representation of a cityscape, typically encompassing many of its features in a single scene.
- 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_69a2e847df8481909239ec08ccf1e376 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f13096248190a622a58dcf540b00 |
completed | Feb. 28, 2026, 1:44 p.m. |
| PD | Predicate disambiguation | batch_69a2edfbb7e0819092cf29c2c68fe8fb |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.