Triple
T5099656
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | InterRegio |
E114951
|
entity |
| Predicate | stopsPattern |
P61097
|
FINISHED |
| Object | more stops than InterCity |
—
|
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: more stops than InterCity | Statement: [InterRegio, stopsPattern, more stops than InterCity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: stopsPattern Context triple: [InterRegio, stopsPattern, more stops than InterCity]
-
A.
stopPatternAtNewBrunswick
Indicates that a service or route pattern includes a stop at New Brunswick as one of its scheduled stopping points.
-
B.
stoppedAt
Indicates that an entity has come to a halt or pause at a specific location or point in time.
-
C.
skipStopServiceBrand
Indicates that a transit service intentionally bypasses a particular stop associated with a given service brand or operator.
-
D.
hasStopType
Indicates that a stop or stopping point is classified as having a particular type or category of stop.
-
E.
majorStop
Indicates that a location functions as a primary or significant stop along a route or service path, typically where vehicles regularly halt for boarding, alighting, or key operations.
- 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_69bd443fc49c819089629c00e311310c |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7568e9c881909f114973faef6832 |
completed | March 20, 2026, 4:27 p.m. |
| PD | Predicate disambiguation | batch_69bd715e06808190931934dc9930f997 |
completed | March 20, 2026, 4:10 p.m. |
| PDg | Predicate description generation | batch_69bd738ac2e0819099c06cdcc5e21d28 |
completed | March 20, 2026, 4:19 p.m. |
Created at: March 20, 2026, 1:40 p.m.