Triple
T22880289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean Giraud |
E567445
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Blueberry |
—
|
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: Blueberry | Statement: [Jean Giraud, notableWork, Blueberry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blueberry Context triple: [Jean Giraud, notableWork, Blueberry]
-
A.
Blueberry
Blueberry is a translucent light-blue color variant famously used on Apple’s early iMac G3 computers.
-
B.
Blueberry
chosen
Blueberry is a Western comic book series co-created by French artist Jean Giraud (Moebius), following the adventures of antihero cavalry officer Mike Blueberry in the American Old West.
-
C.
Blueberries
"Blueberries" is a poem by Robert Frost that vividly portrays rural life and the labor of berry-picking in New England.
-
D.
Berry
Berry is a small historic town on the South Coast of New South Wales, Australia, known for its rural charm, heritage buildings, and popular weekend tourism.
-
E.
Berry
Berry is a historic province in central France known for its rural landscapes, medieval heritage, and traditional French culture.
- 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_69e2458a92ec81908fc1cd5f6407d2ab |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17f5b1ea481909a31a8ed6792ad04 |
completed | April 29, 2026, 3:47 a.m. |
Created at: April 17, 2026, 3:39 p.m.