Triple
T19833243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Wayne as Wil Andersen |
E476514
|
entity |
| Predicate | leadsCattleDrive |
P137502
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [John Wayne as Wil Andersen, leadsCattleDrive, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadsCattleDrive Context triple: [John Wayne as Wil Andersen, leadsCattleDrive, yes]
-
A.
leadsToTrainingAt
Indicates that one entity causes, results in, or serves as a pathway to another entity undergoing training.
-
B.
leadsAstray
Indicates that one entity causes another entity to deviate from a correct, moral, or intended path or course of action.
-
C.
leadsInto
Indicates that one entity serves as an entry or transition point that directly connects or opens into another entity.
-
D.
trainingLeadsTo
Indicates that a process of training results in or brings about a particular outcome, state, or effect.
-
E.
helpsLead
Indicates that one entity assists or contributes to another entity’s act of leading or guiding.
- 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_69d8e51c7c188190b926f3a2a7b5f881 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e656cf7e488190b4be28b5e7b363bf |
completed | April 20, 2026, 4:39 p.m. |
| PD | Predicate disambiguation | batch_69e5305bda388190a23b7191768107b1 |
completed | April 19, 2026, 7:43 p.m. |
| PDg | Predicate description generation | batch_69e532bcf41c8190b685b5adf46a60fc |
completed | April 19, 2026, 7:53 p.m. |
Created at: April 10, 2026, 1:50 p.m.