Triple
T15627106
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laramie Seymour Sullivan |
E375706
|
entity |
| Predicate | hasMotives |
P6699
|
FINISHED |
| Object | hidden motives |
—
|
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: hidden motives | Statement: [Laramie Seymour Sullivan, hasMotives, hidden motives]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMotives Context triple: [Laramie Seymour Sullivan, hasMotives, hidden motives]
-
A.
hasMotiveOfCriminals
Indicates that the specified motive is attributed to or associated with the criminals in question.
-
B.
hasMotiveElement
Indicates that one entity includes, specifies, or is characterized by a particular motive-related component or factor in a broader relationship or action.
-
C.
hasMotiveContext
Indicates that there is contextual information explaining the reasons or motivations behind an action, event, or relationship.
-
D.
hasMotiveTheme
Indicates that an action, event, or situation is associated with a central motivating theme or underlying driving idea.
-
E.
motive
chosen
Indicates the underlying reason, intention, or driving force that explains why an entity performs or is associated with a particular action or event.
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e9e5e248190ae54cda1fde51efb |
completed | April 16, 2026, 2:51 a.m. |
| PD | Predicate disambiguation | batch_69deda868d4481908f4bce1c64d2902a |
completed | April 15, 2026, 12:23 a.m. |
Created at: April 10, 2026, 4:14 a.m.