Triple
T7773861
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boeing 717-200 |
E179140
|
entity |
| Predicate | rangeWithFullPassengers_km |
P19870
|
FINISHED |
| Object | about 2645 |
—
|
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: about 2645 | Statement: [Boeing 717-200, rangeWithFullPassengers_km, about 2645]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rangeWithFullPassengers_km Context triple: [Boeing 717-200, rangeWithFullPassengers_km, about 2645]
-
A.
range_km
chosen
Indicates the maximum distance, measured in kilometers, over which something can operate, travel, or be effective.
-
B.
trackLengthApproxKm
Indicates that one entity has an approximate track length, measured in kilometers, associated with it.
-
C.
lengthInKm
Indicates that one entity specifies the length or distance of another entity measured in kilometers.
-
D.
maximumPassengerCapacity
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
-
E.
passengerCount
Indicates the number of passengers associated with a given entity, such as a vehicle or trip.
- 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_69c69f30602c819082ab52cd4af5c592 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c70461b3e48190bf1e4d4f9e6bb08e |
completed | March 27, 2026, 10:27 p.m. |
| PD | Predicate disambiguation | batch_69c7016f4ce881909c2e9f610255187b |
completed | March 27, 2026, 10:15 p.m. |
Created at: March 27, 2026, 4:11 p.m.