Triple
T19734130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Raza station |
E473930
|
entity |
| Predicate | hasTransferTunnel |
P21069
|
FINISHED |
| Object | pedestrian tunnel between Line 3 and Line 5 platforms |
—
|
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: pedestrian tunnel between Line 3 and Line 5 platforms | Statement: [La Raza station, hasTransferTunnel, pedestrian tunnel between Line 3 and Line 5 platforms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTransferTunnel Context triple: [La Raza station, hasTransferTunnel, pedestrian tunnel between Line 3 and Line 5 platforms]
-
A.
hasTunnel
chosen
Indicates that one entity possesses, contains, or is connected by a tunnel to another entity.
-
B.
usesTunnel
Indicates that one entity makes use of a tunnel as a passage or route to reach or connect to another entity.
-
C.
hasServiceTunnel
Indicates that one entity is connected to or accessed via a dedicated service tunnel associated with another entity.
-
D.
hasDuplexTunnel
Indicates that there exists a bidirectional (two-way) tunnel connection between two entities.
-
E.
hasTunnelShape
Indicates that something possesses a form or configuration resembling a tunnel, typically elongated, enclosed, and passage-like.
- 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_69d8e517ebd48190979ee76723bcfadf |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6515b4d308190af3be1787fa7c65b |
completed | April 20, 2026, 4:16 p.m. |
| PD | Predicate disambiguation | batch_69e5304a7aac8190ac13f75f0c008e45 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:47 p.m.