Triple
T21270535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike Werb |
E524243
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Curious George |
—
|
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: Curious George | Statement: [Mike Werb, notableWork, Curious George]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Curious George Context triple: [Mike Werb, notableWork, Curious George]
-
A.
Curious George
chosen
Curious George is a classic children's book and animated television character, a mischievous little monkey whose curious adventures teach gentle lessons to young audiences.
-
B.
The Cat in the Hat
The Cat in the Hat is a popular dark ride at Universal's Islands of Adventure that brings Dr. Seuss's classic children's book to life through whimsical scenes and motion-based vehicles.
-
C.
George to the Rescue
George to the Rescue is a musical cue from Alan Silvestri’s iconic Back to the Future film score, accompanying a key rescue sequence in the movie.
-
D.
Boo-Boo Bear
Boo-Boo Bear is a gentle, bow-tie-wearing cartoon bear best known as Yogi Bear’s loyal sidekick in the classic Hanna-Barbera animated series.
-
E.
Little Bear
Little Bear is a film production company known for its involvement in notable French cinema projects.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b516293c819089458ea2ec85f85e |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e73651c9208190a87d45acd6fafaaa |
completed | April 21, 2026, 8:33 a.m. |
Created at: April 16, 2026, 4:01 p.m.