Triple
T33931582
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Libertad LRT station |
E869909
|
entity |
| Predicate | hasStationConcourses |
P73175
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Libertad LRT station, hasStationConcourses, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStationConcourses Context triple: [Libertad LRT station, hasStationConcourses, yes]
-
A.
hasNumberOfConcourses
Indicates the relationship specifying how many concourses are associated with a given entity.
-
B.
hasSubwayPlatforms
Indicates that an entity is equipped with one or more platforms specifically serving a subway or metro rail system.
-
C.
hasMetroStations
Indicates that a place or area is served by one or more metro (subway) stations.
-
D.
hasStationHall
chosen
Indicates that one entity (typically a station) includes or is associated with a station hall area as part of its structure or facilities.
-
E.
hasRailPlatforms
Indicates that an entity is equipped with one or more rail platforms used for boarding or alighting from trains.
- 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_69f3499a59788190bff762a891471b31 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff76ac40988190a34d858b5472ee2b |
completed | May 9, 2026, 6:02 p.m. |
| PD | Predicate disambiguation | batch_69ff760a90948190a12fcb80e6e3e14b |
completed | May 9, 2026, 5:59 p.m. |
Created at: May 1, 2026, 1:49 a.m.