Triple
T14665127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dornier Do X |
E344348
|
entity |
| Predicate | recordPassengerLoad |
P29136
|
FINISHED |
| Object | 169 |
—
|
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: 169 | Statement: [Dornier Do X, recordPassengerLoad, 169]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recordPassengerLoad Context triple: [Dornier Do X, recordPassengerLoad, 169]
-
A.
passengerCount
chosen
Indicates the number of passengers associated with a given entity, such as a vehicle or trip.
-
B.
maximumPassengerCapacity
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
-
C.
passengerSystem
Indicates a relationship where an entity functions as or belongs to a passenger-related system (such as a transport or service system designed for passengers).
-
D.
passengers
Indicates that one entity is traveling in or being transported by another entity, typically as a non-operating occupant.
-
E.
passengerCapacityCategory
Indicates the classification of an entity based on the number of passengers it is designed or allowed to carry.
- 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_69d822e283fc8190a0e4c235cf880052 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb54c69f8819080a37161deecfba8 |
completed | April 14, 2026, 9:44 p.m. |
| PD | Predicate disambiguation | batch_69de6576f0208190aa94d995e797ac38 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:27 a.m.