Triple
T3628336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | murder of Jean-Paul Marat |
E76893
|
entity |
| Predicate | hasImmediateConsequence |
P812
|
FINISHED |
| Object | death of Jean-Paul Marat |
—
|
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: death of Jean-Paul Marat | Statement: [murder of Jean-Paul Marat, hasImmediateConsequence, death of Jean-Paul Marat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasImmediateConsequence Context triple: [murder of Jean-Paul Marat, hasImmediateConsequence, death of Jean-Paul Marat]
-
A.
hasConsequence
chosen
Indicates that one event, action, or condition leads to or results in another as its outcome or effect.
-
B.
hasUpstreamImpactOn
Indicates that one entity’s actions, changes, or outputs affect another entity that is positioned earlier in a process, flow, or dependency chain.
-
C.
hasSubsequentInfluence
Indicates that one entity has an influence or effect that occurs after, and is causally or temporally downstream from, another entity or event.
-
D.
hasIntendedEffect
Indicates that one entity is expected or designed to produce a particular effect or outcome on another entity or context.
-
E.
hasSubsequent
Indicates that one entity occurs, appears, or is positioned after another in a defined sequence or order.
- 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_69ad85dc03948190b35b7189e4175bcc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc2df2b708190afef6925a53ec551 |
completed | March 8, 2026, 6:41 p.m. |
| PD | Predicate disambiguation | batch_69adb8410a5881909c94818d7060b2b0 |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:23 p.m.