Triple
T12913597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sweet P |
E308920
|
entity |
| Predicate | hasAlias |
P455
|
FINISHED |
| Object | Sweet P |
E308920
|
NE 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: Sweet P | Statement: [Sweet P, hasAlias, Sweet P]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sweet P Context triple: [Sweet P, hasAlias, Sweet P]
-
A.
Sweet P
chosen
Sweet P is a gentle, childlike character in Adventure Time who embodies the reborn, innocent form of the once-evil Lich.
-
B.
Sweetness
Sweetness is a central character in Toni Morrison’s novel "God Help the Child," known as the light-skinned mother whose harsh treatment of her dark-skinned daughter explores themes of colorism, shame, and maternal love.
-
C.
Sweetness
Sweetness was the iconic nickname of Walter Payton, the legendary Chicago Bears running back widely regarded as one of the greatest players in NFL history.
-
D.
Sweetness
Sweetness is a charismatic and stylish roller skater character from the 2005 film "Roll Bounce," known for being the reigning champion at the local roller rink.
-
E.
Sweetness
Sweetness is a novel by Swedish author Torgny Lindgren, known for its darkly comic, allegorical exploration of human frailty and moral decay in a rural setting.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971a0d6508190bca9668e9e06abfe |
completed | April 10, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6af5df0408190a8fe83cdd91e38c9 |
completed | May 3, 2026, 2:13 a.m. |
Created at: April 9, 2026, 5:41 p.m.