Triple
T26650762
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Penny Marko |
E669045
|
entity |
| Predicate | motivationType |
P7916
|
FINISHED |
| Object | family-related motivation for a villain |
—
|
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: family-related motivation for a villain | Statement: [Penny Marko, motivationType, family-related motivation for a villain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: motivationType Context triple: [Penny Marko, motivationType, family-related motivation for a villain]
-
A.
motivationFor
Indicates that one entity serves as the reason, drive, or incentive behind another entity’s action, state, or occurrence.
-
B.
motivatedByGoal
Indicates that an action, behavior, or state occurs as a result of an intention to achieve a specific goal or desired outcome.
-
C.
laterMotivation
Indicates that one event, state, or action serves as a motivation or reason for another event, state, or action that occurs later in time.
-
D.
motivated
Indicates that one entity provides a reason, drive, or incentive that causes another entity to act or behave in a certain way.
-
E.
typeOfIncentive
chosen
Indicates the specific kind or category of incentive associated with an entity or action.
- 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_69ee9d00eb5481908d6c6d0ada2f0c9a |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f62d53ad58819080c5227c7a729d15 |
completed | May 2, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69f62c15952881908a5ea0c25904afec |
completed | May 2, 2026, 4:53 p.m. |
Created at: April 27, 2026, 2:33 a.m.