Triple
T1350639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guadalajara light rail system |
E28872
|
entity |
| Predicate | corridorType |
P26881
|
FINISHED |
| Object | key urban transport corridors |
—
|
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: key urban transport corridors | Statement: [Guadalajara light rail system, corridorType, key urban transport corridors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: corridorType Context triple: [Guadalajara light rail system, corridorType, key urban transport corridors]
-
A.
hasCorridor
Indicates that one entity includes, is connected by, or provides access through a corridor to another entity.
-
B.
lengthOfCorridors
Indicates the measured extent or distance of corridors within a given space or structure.
-
C.
numberOfCorridors
Indicates the total count of corridors associated with or contained within a given entity or structure.
-
D.
chamberType
Indicates the specific kind or category of chamber associated with an entity (e.g., room, compartment, or enclosed space type).
-
E.
doorType
Indicates the specific kind or category of door associated with an entity.
- 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_69a498571d248190a0ac9eb02d97097f |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c26981d081909ca3b8d8cdf7cf2e |
completed | March 1, 2026, 10:49 p.m. |
| PD | Predicate disambiguation | batch_69a4bef5857c81909ae984feb85a26ca |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4bf60545c8190901ccfb2cb7c4b41 |
completed | March 1, 2026, 10:36 p.m. |
Created at: March 1, 2026, 7:56 p.m.