Triple
T31655860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Slap Bet |
E807856
|
entity |
| Predicate | depictsFictionalPersona |
P33556
|
FINISHED |
| Object | Robin Sparkles |
—
|
NE NERFINISHED |
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: Robin Sparkles | Statement: [Slap Bet, depictsFictionalPersona, Robin Sparkles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: depictsFictionalPersona Context triple: [Slap Bet, depictsFictionalPersona, Robin Sparkles]
-
A.
portraysFictionalEntity
chosen
Indicates that one entity depicts, represents, or plays the role of a fictional character or figure.
-
B.
depictsCharacterType
Indicates that one entity visually represents or portrays a character of a specified type or role.
-
C.
depictsPerson
Indicates that one entity visually represents or portrays a specific person.
-
D.
literaryCharacterDepicted
Indicates that a literary character is visually or textually represented in a work such as an image, illustration, or other medium.
-
E.
isFictionalPersonFrom
Indicates that a fictional person originates from or is associated with a particular place or source.
- 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_69f348daf95c81908b4c985b7ddcd0b3 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f7aa699d68819081ed363931894ab3 |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8cec6d48190bebfa884b2f938c0 |
completed | May 3, 2026, 7:58 p.m. |
Created at: April 30, 2026, 10:55 p.m.