Triple
T2132264
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Embraer ERJ 135 |
E46566
|
entity |
| Predicate | typicalRangeWithFullPassengers |
P13474
|
FINISHED |
| Object | approximately 2800 km |
—
|
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: approximately 2800 km | Statement: [Embraer ERJ 135, typicalRangeWithFullPassengers, approximately 2800 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalRangeWithFullPassengers Context triple: [Embraer ERJ 135, typicalRangeWithFullPassengers, approximately 2800 km]
-
A.
maximumPassengerCapacity
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
-
B.
typicalCapacity
Indicates the usual or standard amount, volume, or capability that something is designed or expected to hold, handle, or perform under normal conditions.
-
C.
typicalRange
chosen
Indicates the usual or expected range of values, conditions, or states within which something normally occurs or applies.
-
D.
passengersCountApproximate
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
-
E.
hasPassengerArea
Indicates that an object or vehicle includes a designated area intended for carrying passengers.
- 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_69a88a1626548190ae59a5028c3baa8e |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abbb7b13ac819094d43159fff984cf |
completed | March 7, 2026, 5:45 a.m. |
| PD | Predicate disambiguation | batch_69abb7bf56e481909b0f497d238451cc |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:44 p.m.