Triple
T15469868
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coffee Town |
E372130
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object | Coffee Town |
E372130
|
NE FINISHED |
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: Coffee Town | Statement: [Coffee Town, title, Coffee Town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coffee Town Context triple: [Coffee Town, title, Coffee Town]
-
A.
Coffee Town
chosen
Coffee Town is a 2013 comedy film about three friends trying to save their favorite coffee shop from being turned into a bar.
-
B.
Chocolate City
Chocolate City is a popular nickname for Washington, D.C., highlighting its historically large and influential African American population and culture.
-
C.
The Cafe
The Cafe is a casual dining spot where people can relax, socialize, and enjoy beverages and light meals.
-
D.
The Coffeehouse
The Coffeehouse is the English title of the Italian play "Il Caffè," a satirical work associated with the Enlightenment-era Milanese literary circle.
-
E.
Kitchen Town
Kitchen Town is a famous Tokyo shopping street district renowned for its many stores specializing in kitchenware, restaurant supplies, and food-related tools.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d85cc8bd308190886949510b42e764 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f6b49788190b270fdfe92646842 |
completed | April 16, 2026, 1:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff2d03845c8190bc8cb96827a5da39 |
completed | May 9, 2026, 12:48 p.m. |
Created at: April 10, 2026, 3:33 a.m.