Triple
T19091394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cougar |
E467292
|
entity |
| Predicate | appearsAlongside |
P25756
|
FINISHED |
| Object | Pooch |
—
|
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: Pooch | Statement: [Cougar, appearsAlongside, Pooch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pooch Context triple: [Cougar, appearsAlongside, Pooch]
-
A.
Pooch
chosen
Pooch is a skilled and resourceful member of the elite black-ops team in the action film "The Losers."
-
B.
Butch Pooch
Butch Pooch is a minor character from the Candyland plantation setting in Quentin Tarantino’s film "Django Unchained," known for serving as one of Calvin Candie’s henchmen.
-
C.
Doggie
Doggie is a nickname of Tony Pérez, the Hall of Fame Cuban-American first baseman best known for his years with the Cincinnati Reds' "Big Red Machine."
-
D.
Fido
Fido is a 2006 Canadian zombie comedy film in which Carrie-Anne Moss plays a lead role in a 1950s-style world where domesticated zombies serve humans.
-
E.
Fido
Fido is a lesser-known companion character associated with the American folk hero Paul Bunyan in tall tales of the lumberjack’s adventures.
- 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_69d8dd05ac4c8190b1967d8f97f3fb2f |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e34b6b348190bb868356ed8b655a |
completed | April 20, 2026, 8:26 a.m. |
Created at: April 10, 2026, 12:04 p.m.