Triple
T11430706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Be Our Guest |
E270870
|
entity |
| Predicate | fictionalPerformer |
P25662
|
FINISHED |
| Object | Cogsworth |
E668465
|
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: Cogsworth | Statement: [Be Our Guest, fictionalPerformer, Cogsworth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cogsworth Context triple: [Be Our Guest, fictionalPerformer, Cogsworth]
-
A.
Cogsworth
chosen
Cogsworth is the pompous yet endearing enchanted mantel clock who serves as the Beast’s strict but loyal majordomo in Disney’s Beauty and the Beast.
-
B.
Hoggle
Hoggle is a gruff yet ultimately loyal dwarf-like creature from the fantasy film "Labyrinth," who helps guide the protagonist through the maze.
-
C.
Gaston
Gaston is a masculine given name of French origin commonly used in Francophone countries and beyond.
-
D.
Edgar Frog
Edgar Frog is a comic-book-obsessed teenage vampire hunter from the horror-comedy film "The Lost Boys," known for his dead-serious attitude and monster-fighting expertise.
-
E.
Douzy
Douzy is a small commune in the Ardennes department of northern France, known for its rural character and cross-border ties, including a town twinning with Kaiserslautern in Germany.
- 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_69d6aadeef688190874bcecd88b3dd9b |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d806c1bfb881909720c74fe0fa837f |
completed | April 9, 2026, 8:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5b8d923688190bb4d61d57768e10e |
completed | April 20, 2026, 5:25 a.m. |
Created at: April 8, 2026, 9:35 p.m.