Triple
T20107342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barking at Airplanes |
E490216
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Bon Voyage |
—
|
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: Bon Voyage | Statement: [Barking at Airplanes, hasPart, Bon Voyage]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bon Voyage Context triple: [Barking at Airplanes, hasPart, Bon Voyage]
-
A.
Bon Voyage
chosen
"Bon Voyage" is a song by the New Zealand rock band Oceanic.
-
B.
Bon Voyage
Bon Voyage is a 2003 French romantic thriller film, directed by Jean-Paul Rappeneau, that intertwines political intrigue and personal drama on the eve of World War II.
-
C.
Bon Voyage
Bon Voyage is a retail store, likely themed around travel, that offers related goods and accessories to customers.
-
D.
Le Voyage
"Le Voyage" is the concluding, philosophically charged poem in Charles Baudelaire's collection *Les Fleurs du mal*, exploring themes of escape, disillusionment, and the search for the unknown.
-
E.
Le Long Voyage
Le Long Voyage is a French science fiction work by Gérard Klein that explores themes of space travel and human destiny.
- 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_69da62636cc08190982cc71733a17b8d |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e666dcb8d4819091889e19dd9137a6 |
completed | April 20, 2026, 5:48 p.m. |
Created at: April 11, 2026, 11:28 p.m.