Triple
T29448112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CSeries |
E746904
|
entity |
| Predicate | typicalSeatingCS300 |
P2608
|
FINISHED |
| Object | 130–150 passengers |
—
|
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: 130–150 passengers | Statement: [CSeries, typicalSeatingCS300, 130–150 passengers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSeatingCS300 Context triple: [CSeries, typicalSeatingCS300, 130–150 passengers]
-
A.
typicalSeat
Indicates the usual or standard seating position or location associated with an entity in a given context.
-
B.
hasSeating
chosen
Indicates that one entity provides or contains seating capacity or seating arrangements for another entity.
-
C.
coSeatIn
Indicates that two or more entities share the same seat or seating location at the same time.
-
D.
thirdPlaceSeatCount
Indicates the number of seats allocated to the entity that finished in third place in a given ranking or competition.
-
E.
seatingConfiguration
Indicates how seats are arranged or organized relative to each other in a given context.
- 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_69f0a7a230488190b44a97fe3d16f731 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_6a01c303af6081909c229b2c8e91c680 |
completed | May 11, 2026, 11:52 a.m. |
| PD | Predicate disambiguation | batch_6a01c28ebf54819094318c447d8c7943 |
completed | May 11, 2026, 11:50 a.m. |
Created at: April 28, 2026, 3:29 p.m.