Triple
T7727016
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Claire Coffee |
E175155
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Coffee
Coffee is a popular brewed beverage made from roasted coffee beans, known for its stimulating caffeine content and rich, diverse flavors.
|
E683847
|
NE FINISHED |
How this triple was built (4 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 | Statement: [Claire Coffee, familyName, Coffee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coffee Context triple: [Claire Coffee, familyName, Coffee]
-
A.
Coffee
"Coffee" is a song by the American singer-songwriter Miguel, known for its smooth blend of R&B and sensual, atmospheric production.
-
B.
Caffe
Caffe is an open-source deep learning framework known for its speed and modular design, widely used in computer vision research and applications.
-
C.
Caffeine
"Caffeine" is a young adult novel by Sharon Robinson that explores themes of adolescence, family, and personal struggle.
-
D.
Chai
Chai is a popular JavaScript assertion library commonly used in testing frameworks like Mocha to provide expressive, readable test assertions.
-
E.
Café au Lait
Café au Lait is one of the short, conversational vignettes in Jim Jarmusch’s film "Coffee and Cigarettes," featuring characters chatting over coffee in a minimalist, black-and-white setting.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Coffee Triple: [Claire Coffee, familyName, Coffee]
Generated description
Coffee is a popular brewed beverage made from roasted coffee beans, known for its stimulating caffeine content and rich, diverse flavors.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Coffee Target entity description: Coffee is a popular brewed beverage made from roasted coffee beans, known for its stimulating caffeine content and rich, diverse flavors.
-
A.
Coffee
"Coffee" is a song by the American singer-songwriter Miguel, known for its smooth blend of R&B and sensual, atmospheric production.
-
B.
Caffe
Caffe is an open-source deep learning framework known for its speed and modular design, widely used in computer vision research and applications.
-
C.
Caffeine
"Caffeine" is a young adult novel by Sharon Robinson that explores themes of adolescence, family, and personal struggle.
-
D.
Chai
Chai is a popular JavaScript assertion library commonly used in testing frameworks like Mocha to provide expressive, readable test assertions.
-
E.
Café au Lait
Café au Lait is one of the short, conversational vignettes in Jim Jarmusch’s film "Coffee and Cigarettes," featuring characters chatting over coffee in a minimalist, black-and-white setting.
- F. None of above. chosen
Provenance (5 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_69c6995d541c81909eaa646b1a8369a9 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c70314abb88190a7eaa519bd7398c9 |
completed | March 27, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b5275164819096678c019fdd4da4 |
completed | March 29, 2026, 5:14 a.m. |
| NEDg | Description generation | batch_69c8b5ed16d48190b127877fc9a11abf |
completed | March 29, 2026, 5:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8b65a04a48190bf5e01ba0921cf14 |
completed | March 29, 2026, 5:19 a.m. |
Created at: March 27, 2026, 4:06 p.m.