Triple
T16065022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U-Bahn line U3 |
E389708
|
entity |
| Predicate | connectsResidentialAreasWith |
P112694
|
FINISHED |
| Object | central transport hubs |
—
|
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: central transport hubs | Statement: [U-Bahn line U3, connectsResidentialAreasWith, central transport hubs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsResidentialAreasWith Context triple: [U-Bahn line U3, connectsResidentialAreasWith, central transport hubs]
-
A.
connectsResidentialArea
Indicates a relationship where something serves as a link or route between one residential area and another.
-
B.
connectsArea
Indicates that one area serves as a link or passage between two other areas, enabling movement or interaction between them.
-
C.
connectsNeighborhoodsIn
chosen
Indicates a relationship where something (such as a route, road, or service) links or provides direct access between two or more neighborhoods.
-
D.
connectsCentralAreaTo
Indicates a relationship where one element serves as a link or pathway between a central area and another location or component.
-
E.
connectsTypeOfAreas
Indicates a relationship where one entity serves as a link or connector between two different types of areas.
- 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1858a00888190b8505071575dc56f |
completed | April 17, 2026, 12:57 a.m. |
| PD | Predicate disambiguation | batch_69e18272f2288190a17d45fb01cc2b07 |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 4:57 a.m.