Triple
T38046159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boeing T-43 |
E949623
|
entity |
| Predicate | trainingStationsCapacity |
P1301
|
FINISHED |
| Object | 12 |
—
|
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: 12 | Statement: [Boeing T-43, trainingStationsCapacity, 12]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingStationsCapacity Context triple: [Boeing T-43, trainingStationsCapacity, 12]
-
A.
stageCapacity
Indicates the maximum number of people or amount of occupancy that a particular stage can accommodate.
-
B.
numberOfStations
chosen
Indicates the total count of stations associated with or contained by a given entity.
-
C.
trackCapacity
Indicates the maximum number of trains or amount of traffic that a specific track segment is designed to handle within a given time period.
-
D.
operatorCapacity
Indicates the maximum workload or volume of tasks that an operator is able to handle within a given context or time frame.
-
E.
officialCapacities
Indicates that the relationship or action is performed in an entity’s formal, authorized, or institutional role rather than in a personal or informal capacity.
- 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_69f76f000cf081908c11fb5443b392e6 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fc44c7e73c819082d4fc1900fb9632 |
completed | May 7, 2026, 7:52 a.m. |
| PD | Predicate disambiguation | batch_69fbc8efffbc8190a139798ad1880526 |
completed | May 6, 2026, 11:04 p.m. |
Created at: May 3, 2026, 4:20 p.m.