Triple
T35537145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Max und Moritz |
E1026960
|
entity |
| Predicate | numberOfPranks |
P207036
|
FINISHED |
| Object | 7 |
—
|
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: 7 | Statement: [Max und Moritz, numberOfPranks, 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPranks Context triple: [Max und Moritz, numberOfPranks, 7]
-
A.
Prank Encounters
Indicates a relationship where one party orchestrates a deceptive or surprising prank scenario that another party unexpectedly experiences or becomes the target of.
-
B.
notablePrankTarget
Indicates that the subject is a well-known or frequent target of pranks carried out by the object.
-
C.
numberOfPresses
Indicates the total count of times a button or similar control has been pressed.
-
D.
numberOfHells
Indicates the quantity or count of distinct hells associated with a given entity or context.
-
E.
trapNumber
Indicates that an entity is identified as, or associated with, a specific trap number within a system or context.
- 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_69f76dff7e508190b28ceeee770dce23 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a04d8348190a4819666eab42c9b |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82179081908325a59b8539b3a8 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:04 p.m.