Triple
T32454996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs. Devaney |
E829398
|
entity |
| Predicate | vehicleTypeInvolved |
P1776
|
FINISHED |
| Object | passenger jet |
—
|
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: passenger jet | Statement: [Mrs. Devaney, vehicleTypeInvolved, passenger jet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vehicleTypeInvolved Context triple: [Mrs. Devaney, vehicleTypeInvolved, passenger jet]
-
A.
vehicleType
chosen
Indicates the specific kind or category of vehicle associated with an entity (e.g., car, bus, bicycle).
-
B.
utilityInvolved
Indicates that a utility service or provider is involved in, associated with, or plays a role in the referenced situation or relationship.
-
C.
vehicleUsed
Indicates that a particular vehicle is utilized or employed in performing an action, event, or activity.
-
D.
vehicleFamily
Indicates that two vehicles belong to the same family or category based on shared design, platform, or lineage.
-
E.
vehicleTypeFocus
Indicates that the relationship or action specifically concerns or emphasizes a particular type or category of vehicle.
- 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_69f3491df9288190afc0b23b1d6e72ce |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c3155c448190b71704970c2cf0e6 |
completed | May 3, 2026, 3:37 a.m. |
| PD | Predicate disambiguation | batch_69f6ba700a708190ab6db62791e43774 |
completed | May 3, 2026, 3:01 a.m. |
Created at: May 1, 2026, 12:56 a.m.