Triple
T29428036
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | April Fool's Day (2008 film) |
E746348
|
entity |
| Predicate | hasTargetVictims |
P170741
|
FINISHED |
| Object | friends involved in prank |
—
|
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: friends involved in prank | Statement: [April Fool's Day (2008 film), hasTargetVictims, friends involved in prank]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTargetVictims Context triple: [April Fool's Day (2008 film), hasTargetVictims, friends involved in prank]
-
A.
hasVictimCount
Indicates the number of victims associated with a particular event, action, or entity.
-
B.
hasVictimsCount
Indicates the number of victims associated with a particular event, action, or entity.
-
C.
hasMainVictim
Indicates that an event, action, or harmful situation primarily targets or affects a specific victim as its main subject.
-
D.
hasVictims
Indicates that an entity has one or more individuals who have been harmed, injured, or adversely affected by it.
-
E.
numberOfVictimsTargeted
Indicates the quantity of victims that were intended or selected as targets in the referenced act or event.
- 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_69f0a7a06e0081908add494075912eb4 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69f695f9fe7c819084322bf6cdc70a13 |
completed | May 3, 2026, 12:25 a.m. |
| PD | Predicate disambiguation | batch_69f690ed5d008190831cf8e44cce28af |
completed | May 3, 2026, 12:03 a.m. |
| PDg | Predicate description generation | batch_69f695385a2881908cc28ef97fffc867 |
completed | May 3, 2026, 12:22 a.m. |
Created at: April 28, 2026, 3:11 p.m.