Triple
T20384180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toto |
E497917
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Terry the dog |
—
|
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: Terry the dog | Statement: [Toto, portrayedBy, Terry the dog]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Terry the dog Context triple: [Toto, portrayedBy, Terry the dog]
-
A.
Terry (dog)
chosen
Terry was the female Cairn Terrier best known for playing Toto in the 1939 film adaptation of "The Wizard of Oz."
-
B.
Beasley the Dog
Beasley the Dog was the canine actor best known for playing the slobbery Dogue de Bordeaux partner to Tom Hanks in the 1989 film "Turner & Hooch."
-
C.
Tupper the Bulldog
Tupper the Bulldog is the costumed canine figure that represents Bryant University at its athletic events and campus activities.
-
D.
Buster the dog
Buster the dog is a loyal canine companion and recurring supporting character in The Mystery Series, often aiding the protagonists in their investigations.
-
E.
Piper the Dog
Piper the Dog is the costumed canine mascot representing Hamline University at its athletic events and campus activities.
- 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_69e0b4a5b7908190a972e4e7e698ae94 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e678b2ceec819091ad5205ee9b2174 |
completed | April 20, 2026, 7:04 p.m. |
Created at: April 16, 2026, 11:27 a.m.