Triple
T33250970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Petershausen |
E851238
|
entity |
| Predicate | hasGoodTransportConnectionTo |
P130540
|
FINISHED |
| Object | Munich |
—
|
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: Munich | Statement: [Petershausen, hasGoodTransportConnectionTo, Munich]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGoodTransportConnectionTo Context triple: [Petershausen, hasGoodTransportConnectionTo, Munich]
-
A.
hasGoodTransportLinks
Indicates that a place is well connected to other locations by efficient and convenient transport options.
-
B.
hasPublicTransportConnection
Indicates that there is an available public transportation link or service connecting the related entities.
-
C.
hasGoodPublicTransportConnections
chosen
Indicates that an entity is well served by public transportation options, providing convenient and efficient connections to other locations.
-
D.
hasTransitAccessTo
Indicates that one place or entity is reachable from another via public or shared transportation services.
-
E.
hasTransportationRelation
Indicates a relationship in which one entity provides, uses, is connected by, or is otherwise associated with a means or mode of transportation to another entity or location.
- 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_69f34963135c819084e7f1d483421f00 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6f38159d08190980ad639e08f00f4 |
completed | May 3, 2026, 7:04 a.m. |
| PD | Predicate disambiguation | batch_69f6e3d7bee48190b94e0beb48a1d7fa |
completed | May 3, 2026, 5:57 a.m. |
Created at: May 1, 2026, 1:31 a.m.