Triple
T24638111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North American commuter railroads |
E609868
|
entity |
| Predicate | typicalStationLocation |
P21833
|
FINISHED |
| Object | city centers |
—
|
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: city centers | Statement: [North American commuter railroads, typicalStationLocation, city centers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalStationLocation Context triple: [North American commuter railroads, typicalStationLocation, city centers]
-
A.
typicalUseLocation
chosen
Indicates the usual or most common location where an entity is used or operates.
-
B.
originalStationLocation
Indicates the location where a station was initially established or first situated.
-
C.
hasStationNear
Indicates that one entity has a station located in close proximity to another entity.
-
D.
isSuburbanStationOf
Indicates that a station is located in a suburban area and functionally serves as a subsidiary or outlying station of a main or central station.
-
E.
oftenLocatedAs
Indicates that one entity is frequently found or situated in the same place as another entity.
- 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_69e2c4d28f848190ac38c400060e943d |
completed | April 17, 2026, 11:40 p.m. |
| NER | Named-entity recognition | batch_69f2be064ff88190b5d9e5ec75a41242 |
completed | April 30, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f2a6d0ab708190b2e3b94dd20ca76b |
completed | April 30, 2026, 12:48 a.m. |
Created at: April 18, 2026, 2:33 a.m.