Triple
T21616058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weilburg ship tunnel on the Lahn |
E533439
|
entity |
| Predicate | hasTunnelFunction |
P144788
|
FINISHED |
| Object | navigation |
—
|
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: navigation | Statement: [Weilburg ship tunnel on the Lahn, hasTunnelFunction, navigation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTunnelFunction Context triple: [Weilburg ship tunnel on the Lahn, hasTunnelFunction, navigation]
-
A.
hasTunnel
Indicates that one entity possesses, contains, or is connected by a tunnel to another entity.
-
B.
hasServiceTunnel
Indicates that one entity is connected to or accessed via a dedicated service tunnel associated with another entity.
-
C.
hasTunnelShape
Indicates that something possesses a form or configuration resembling a tunnel, typically elongated, enclosed, and passage-like.
-
D.
hasTunnelSections
Indicates that an entity includes or is composed of multiple distinct tunnel segments or portions.
-
E.
hasEmergencyTunnel
Indicates that one location, structure, or system possesses a dedicated emergency tunnel connecting it to another place or route for use in urgent or hazardous situations.
- F. None of above. chosen
Provenance (4 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_69e0c46411108190bba0d4176dffc9f3 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef3baab9e88190bc02f27133ef32d6 |
completed | April 27, 2026, 10:34 a.m. |
| PD | Predicate disambiguation | batch_69e69665fe8c8190af7e38785db188b2 |
completed | April 20, 2026, 9:11 p.m. |
| PDg | Predicate description generation | batch_69e69b4aa2b48190830107391e81571a |
completed | April 20, 2026, 9:31 p.m. |
Created at: April 16, 2026, 6:33 p.m.