Triple
T11330840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ford F-150 Lightning Pro |
E268337
|
entity |
| Predicate | hasCabStyle |
P65309
|
FINISHED |
| Object | SuperCrew |
—
|
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: SuperCrew | Statement: [Ford F-150 Lightning Pro, hasCabStyle, SuperCrew]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCabStyle Context triple: [Ford F-150 Lightning Pro, hasCabStyle, SuperCrew]
-
A.
hasCabType
chosen
Indicates that an entity is associated with or characterized by a specific type or category of cab.
-
B.
hasCabPosition
Indicates that an entity has a specific position or placement of a cab relative to its overall structure or configuration.
-
C.
hasParkStyle
Indicates that an entity possesses or is characterized by a particular style or type of park.
-
D.
hasExteriorStyle
Indicates that an entity possesses or is characterized by a particular exterior design or stylistic appearance.
-
E.
hasStationStyle
Indicates that one entity (typically a station) possesses or is characterized by a particular architectural or design style.
- 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_69d6aacb1f0881908c84a349fd1be047 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9fd38308190a5458be1bfcc89ea |
completed | April 9, 2026, 6:03 p.m. |
| PD | Predicate disambiguation | batch_69d787afe5a48190b8af1a3e19529641 |
completed | April 9, 2026, 11:04 a.m. |
Created at: April 8, 2026, 9:32 p.m.