Triple
T36058828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boeing 777-200ER |
E1043019
|
entity |
| Predicate | typicalSeatingThreeClass |
P84520
|
FINISHED |
| Object | 301 |
—
|
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: 301 | Statement: [Boeing 777-200ER, typicalSeatingThreeClass, 301]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSeatingThreeClass Context triple: [Boeing 777-200ER, typicalSeatingThreeClass, 301]
-
A.
seatClass
Indicates the travel or seating category assigned to a passenger or seat (e.g., economy, business, first class).
-
B.
aircraftSeatingCategory
chosen
Indicates the classification of an aircraft’s seating arrangement or capacity type associated with an entity.
-
C.
typicalSeat
Indicates the usual or standard seating position or location associated with an entity in a given context.
-
D.
thirdPlaceSeatCount
Indicates the number of seats allocated to the entity that finished in third place in a given ranking or competition.
-
E.
classesOfSeats
Indicates the different categories or types of seats associated with something, such as a venue, vehicle, or event.
- 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_69f76e2f09448190b0486d5ecad5e243 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037c8d06cc8190ab6a5e18d9d2571e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0895b48190acdd88dc10db7be7 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:08 p.m.