Triple
T8243880
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trucks |
E192801
|
entity |
| Predicate | primaryAntagonists |
P81119
|
FINISHED |
| Object | driverless trucks |
—
|
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: driverless trucks | Statement: [Trucks, primaryAntagonists, driverless trucks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryAntagonists Context triple: [Trucks, primaryAntagonists, driverless trucks]
-
A.
primaryAntagonistType
Indicates the role or category of the main opposing force or adversary that serves as the central source of conflict.
-
B.
primaryEnemy
Indicates that one entity is the main or most significant adversary or opponent of another entity.
-
C.
antagonistOf
Indicates a relationship where one entity actively opposes, conflicts with, or serves as an adversary to another.
-
D.
hasAntagonisticProtagonist
Indicates that the work features a main character who opposes or undermines the typical heroic or moral expectations of a traditional protagonist.
-
E.
isCentralAntagonist
Indicates that an entity serves as the primary opposing force or main villain driving conflict against the protagonist or central characters.
- 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_69ca82de7b8c81908d8106f8a53cff9b |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb78711f5081909c2f357334491a07 |
completed | March 31, 2026, 7:32 a.m. |
| PD | Predicate disambiguation | batch_69cb36b437e881909958591357e83b9d |
completed | March 31, 2026, 2:51 a.m. |
| PDg | Predicate description generation | batch_69cb447c146081909decf97bbd26c496 |
completed | March 31, 2026, 3:50 a.m. |
Created at: March 30, 2026, 5:47 p.m.