Triple
T8289171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Virtual Network Computing |
E193851
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object |
Vino
Vino is a VNC-compatible remote desktop server for the GNOME desktop environment on Unix-like systems.
|
E724330
|
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: Vino | Statement: [Virtual Network Computing, hasVariant, Vino]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vino Context triple: [Virtual Network Computing, hasVariant, Vino]
-
A.
Wein
Wein is a surname most notably associated with George Wein, the influential American jazz promoter and founder of major music festivals such as the Newport Jazz Festival.
-
B.
Winer
Winer is a surname most notably associated with Dave Winer, an influential software developer and pioneer of blogging and RSS technologies.
-
C.
Le Vin
Le Vin is a section of Charles Baudelaire’s poetry collection Les Fleurs du mal that explores themes of intoxication, escape, and existential despair through the motif of wine.
-
D.
Cartojal wine
Cartojal wine is a sweet, chilled Málaga wine especially popular during the Málaga Fair and associated with festive summer celebrations in southern Spain.
-
E.
Vino Rosso
Vino Rosso is an American Thoroughbred racehorse best known for winning the 2019 Breeders’ Cup Classic and being a top-level dirt router.
- 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: Vino Triple: [Virtual Network Computing, hasVariant, Vino]
Generated description
Vino is a VNC-compatible remote desktop server for the GNOME desktop environment on Unix-like systems.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vino Target entity description: Vino is a VNC-compatible remote desktop server for the GNOME desktop environment on Unix-like systems.
-
A.
Wein
Wein is a surname most notably associated with George Wein, the influential American jazz promoter and founder of major music festivals such as the Newport Jazz Festival.
-
B.
Winer
Winer is a surname most notably associated with Dave Winer, an influential software developer and pioneer of blogging and RSS technologies.
-
C.
Le Vin
Le Vin is a section of Charles Baudelaire’s poetry collection Les Fleurs du mal that explores themes of intoxication, escape, and existential despair through the motif of wine.
-
D.
Cartojal wine
Cartojal wine is a sweet, chilled Málaga wine especially popular during the Málaga Fair and associated with festive summer celebrations in southern Spain.
-
E.
Vino Rosso
Vino Rosso is an American Thoroughbred racehorse best known for winning the 2019 Breeders’ Cup Classic and being a top-level dirt router.
- 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_69ca82e32db481908b72f3804fa71152 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7c98e15c8190ac2a0b2a5ff834c9 |
completed | March 31, 2026, 7:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd68898610819091a76f89cd2a6aa2 |
completed | April 1, 2026, 6:48 p.m. |
| NEDg | Description generation | batch_69cd6d55196881909cf5ec925792e09f |
completed | April 1, 2026, 7:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cd7e2bdae08190adc51e904e85695e |
completed | April 1, 2026, 8:21 p.m. |
Created at: March 30, 2026, 5:52 p.m.