Triple
T29005638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autobahn A67 |
E736423
|
entity |
| Predicate | connectsMetropolitanRegion |
P80197
|
FINISHED |
| Object | Rhine-Main |
—
|
NE NERFINISHED |
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: Rhine-Main | Statement: [Autobahn A67, connectsMetropolitanRegion, Rhine-Main]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsMetropolitanRegion Context triple: [Autobahn A67, connectsMetropolitanRegion, Rhine-Main]
-
A.
connectsMetroAreas
Indicates a relationship where a transportation route or service links two or more metropolitan areas, enabling direct travel or interaction between them.
-
B.
connectsRegionalCity
Indicates a relationship where one entity serves as a link or transport route between a regional city and another location.
-
C.
hasMetropolitanConnectionWith
Indicates that there is a significant relationship or linkage between two entities based on shared or interacting metropolitan areas, such as through infrastructure, services, or regional integration.
-
D.
belongsToMetropolitanRegion
Indicates that one geographic or administrative area is part of, or included within, a larger metropolitan region.
-
E.
linksMetropolitanArea
chosen
Indicates a relationship where one entity connects or associates a subject with a specific metropolitan 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_69f077eb81e88190ad9ff62cbb9f555e |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69fd68abf52881909c5a390c362b7c59 |
completed | May 8, 2026, 4:38 a.m. |
| PD | Predicate disambiguation | batch_69fd6812d0c88190930d8fa2d4b92490 |
completed | May 8, 2026, 4:35 a.m. |
Created at: April 28, 2026, 9:37 a.m.