Triple
T19579542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ken Scott |
E489949
|
entity |
| Predicate | wrote |
P2831
|
FINISHED |
| Object | Starbuck |
—
|
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: Starbuck | Statement: [Ken Scott, wrote, Starbuck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Starbuck Context triple: [Ken Scott, wrote, Starbuck]
-
A.
Starbuck
Starbuck is the morally conscientious first mate of the whaling ship Pequod in Herman Melville’s novel "Moby-Dick," serving as a cautious and ethical counterpoint to Captain Ahab’s obsessive quest.
-
B.
Starbuck
Starbuck is the callsign of Kara Thrace, a hotshot Viper pilot and central character in the reimagined Battlestar Galactica television series.
-
C.
Starbuck
chosen
Starbuck is a 2011 Canadian comedy film about a man who discovers he has fathered hundreds of children through sperm donations and must confront the consequences when many of them seek to learn his identity.
-
D.
Starbucks
Starbucks is a global coffeehouse chain and coffee roastery brand known for its specialty coffee drinks and widespread presence in cities around the world.
-
E.
McCafé
McCafé is McDonald's in-house coffeehouse-style chain offering specialty coffee drinks, pastries, and café-style food items.
- 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_69d8e8dd9374819098e36349b3211663 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6402763b8819099e535979f094f9d |
completed | April 20, 2026, 3:03 p.m. |
Created at: April 10, 2026, 1:42 p.m.