Triple
T10575232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TRAM |
E249591
|
entity |
| Predicate | connectsUrbanAreaType |
P90780
|
FINISHED |
| Object | residential areas |
—
|
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: residential areas | Statement: [TRAM, connectsUrbanAreaType, residential areas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsUrbanAreaType Context triple: [TRAM, connectsUrbanAreaType, residential areas]
-
A.
connectsCity
Indicates a relationship where one entity serves as a link or route that joins or provides direct access between two cities.
-
B.
connectsTypeOfCity
Indicates a relationship where one entity is linked to another as a specific type or category of city.
-
C.
formsUrbanAreaWith
Indicates that two or more settlements are geographically and functionally connected so that together they constitute a single continuous urban area.
-
D.
urbanAreaType
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
-
E.
appliesToUrbanAreaType
chosen
Indicates that something (such as a rule, measure, or classification) is applicable specifically to a particular type or category of urban area.
- 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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5274b100c8190b477ef4cb745c269 |
completed | April 7, 2026, 3:48 p.m. |
| PD | Predicate disambiguation | batch_69d51901ff6c819095e7b528170a69dc |
completed | April 7, 2026, 2:47 p.m. |
Created at: April 6, 2026, 12:38 p.m.