Triple
T15001174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tuka trial of 1929 |
E374091
|
entity |
| Predicate | impactOnDefendant |
P110997
|
FINISHED |
| Object | imprisonment of Vojtech Tuka |
—
|
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: imprisonment of Vojtech Tuka | Statement: [Tuka trial of 1929, impactOnDefendant, imprisonment of Vojtech Tuka]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactOnDefendant Context triple: [Tuka trial of 1929, impactOnDefendant, imprisonment of Vojtech Tuka]
-
A.
impactOnDefendants
chosen
Indicates the effect or consequences that an action, decision, or condition has on the defendants.
-
B.
impactOnLaw
Indicates the effect or influence that one entity, event, or action has on laws, legal rules, or the legal system.
-
C.
impactOutcome
Indicates that one entity produces an effect or influence that changes the result, consequence, or final state of another entity or situation.
-
D.
impactOnOwner
Indicates that one entity has an effect, influence, or consequence on the owner entity.
-
E.
impactDescription
Indicates a description of the effect, consequence, or influence that one entity, action, or event has on another.
- 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_69d85ccc84388190aa151e5173370c8d |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded72fec948190b1c9705538c57976 |
completed | April 15, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69de9a6531a88190acde65199a477350 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:54 a.m.