Triple
T1377303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dodge |
E29253
|
entity |
| Predicate | hasModelLine |
P27119
|
FINISHED |
| Object | SRT performance models |
—
|
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: SRT performance models | Statement: [Dodge, hasModelLine, SRT performance models]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasModelLine Context triple: [Dodge, hasModelLine, SRT performance models]
-
A.
hasAssemblyLineFor
Indicates that one entity operates or contains an assembly line specifically used to produce, process, or assemble another entity.
-
B.
hasLineNumber
Indicates that something is associated with a specific line number, typically denoting its position within an ordered sequence such as lines of text or code.
-
C.
hasModelType
Indicates that an entity is associated with or classified under a specific model type.
-
D.
containsLine
Indicates that one entity includes or encloses a specific line within its spatial or structural extent.
-
E.
hasIconicLine
Indicates that an entity (such as a work or character) is associated with a particularly famous or memorable line of dialogue.
- F. None of above. chosen
Provenance (4 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_69a498d883a48190bfdca525296ef7ee |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c31602b8819087a57e8d390cae7a |
completed | March 1, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69a4befcabdc8190a9f05d002603f81c |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c0335f7081908d50046ced4cdee0 |
completed | March 1, 2026, 10:39 p.m. |
Created at: March 1, 2026, 7:59 p.m.