Triple
T7924480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokyo Disney Resort Toy Story Hotel |
E184025
|
entity |
| Predicate | hasCharacterPresence |
P79809
|
FINISHED |
| Object | Jessie |
E237529
|
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: Jessie | Statement: [Tokyo Disney Resort Toy Story Hotel, hasCharacterPresence, Jessie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jessie Context triple: [Tokyo Disney Resort Toy Story Hotel, hasCharacterPresence, Jessie]
-
A.
Jessie
chosen
Jessie is a spirited, yodeling cowgirl doll from the Toy Story franchise known for her energetic personality and emotional backstory.
-
B.
Jessie
Jessie is a given name associated with the acclaimed British-American actress Jessica Tandy, known for her distinguished stage and film career.
-
C.
Jessie
Jessie is the given name of Jessie James Combs, an American television personality and professional racer known for her work on automotive and metal fabrication shows.
-
D.
Jessie
Jessie is a person whose full name is Jessie Oriana Huxley.
-
E.
Jessie
Jessie is a given name commonly used as a diminutive or variant of names like Jessica or Jesse.
- 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_69ca828fe7bc819090f52c88dcd72183 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3aac02bc8190b13fc354a4fa91d3 |
completed | March 31, 2026, 3:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5bf3c3288190ac3df917be92f9f8 |
completed | March 31, 2026, 5:30 a.m. |
Created at: March 30, 2026, 5:06 p.m.