Triple
T19512393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clown Chocolat |
E488187
|
entity |
| Predicate | hasTitle |
P38
|
FINISHED |
| Object | Clown Chocolat |
E488187
|
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: Clown Chocolat | Statement: [Clown Chocolat, hasTitle, Clown Chocolat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Clown Chocolat Context triple: [Clown Chocolat, hasTitle, Clown Chocolat]
-
A.
Clown Chocolat
chosen
Clown Chocolat is a painting by French artist Jean Dubuffet that exemplifies his raw, unconventional style associated with Art Brut.
-
B.
Bon-Bon
Bon-Bon is a common affectionate nickname, often used for people named Bonnie or for characters in popular media.
-
C.
Chocolate Bear
Chocolate Bear is the affectionate nickname of Dr. Christopher Turk, a main character and surgeon on the television series "Scrubs."
-
D.
Dame Chocolate
Dame Chocolate is a Spanish-language telenovela best known for starring Genesis Rodriguez in a leading role.
-
E.
Chouchou
Chouchou was the affectionate nickname of Claude Debussy’s young daughter, to whom he dedicated his piano suite "Children’s Corner."
- 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_69d8e8da8bec819081f400199491ccc3 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6359908fc8190bd05f26d4271d268 |
completed | April 20, 2026, 2:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0747235758819086d85c107b187b7e |
completed | May 15, 2026, 4:17 p.m. |
Created at: April 10, 2026, 1:40 p.m.