Triple
T20982966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seville commuter rail |
E516810
|
entity |
| Predicate | connectsUrbanCoreWith |
P97106
|
FINISHED |
| Object | Seville suburbs |
—
|
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: Seville suburbs | Statement: [Seville commuter rail, connectsUrbanCoreWith, Seville suburbs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsUrbanCoreWith Context triple: [Seville commuter rail, connectsUrbanCoreWith, Seville suburbs]
-
A.
connectsWorks
Indicates a relationship where one work serves to link, bridge, or associate two or more other works.
-
B.
connectsCity
Indicates a relationship where one entity serves as a link or route that joins or provides direct access between two cities.
-
C.
hasUrbanConcept
Indicates that an entity is associated with, characterized by, or incorporates an urban-related concept, idea, or design principle.
-
D.
connectsToUrbanCenter
chosen
Indicates that one entity has a direct or functional linkage to an urban center, such as through infrastructure, services, or regular interaction.
-
E.
hasUrbanFabric
Indicates that one entity possesses, contains, or is characterized by a particular pattern or structure of built-up urban development.
- 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_69e0b4ffac148190bbade9f0eceb660b |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6fbe03244819097630333e70c4e88 |
completed | April 21, 2026, 4:24 a.m. |
| PD | Predicate disambiguation | batch_69e5dbe6976081908abd4e9c8734bae9 |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 16, 2026, 1:48 p.m.