Triple
T2322132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viva Aerobus |
E48204
|
entity |
| Predicate | loyaltyProgram |
P178
|
FINISHED |
| Object |
Viva Fan
Viva Fan is the frequent-flyer loyalty program of Mexican low-cost airline Viva Aerobus, offering members points, discounts, and travel-related benefits.
|
E256643
|
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: Viva Fan | Statement: [Viva Aerobus, loyaltyProgram, Viva Fan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Viva Fan Context triple: [Viva Aerobus, loyaltyProgram, Viva Fan]
-
A.
Viva
Viva is a bus rapid transit service in York Region, Ontario, Canada, providing frequent, limited-stop public transportation along major corridors.
-
B.
Viva
Viva is a German music television channel that gained popularity in the 1990s and 2000s for its music videos, pop culture programming, and youth-oriented shows.
-
C.
Toni Vega
Toni Vega is a musical artist known for contributing featured vocals to tracks such as "Monkey Business."
-
D.
Thalia
Thalia is one of the nine Muses in Greek mythology, traditionally associated with comedy and pastoral poetry.
-
E.
Vivanco
Vivanco is a Spanish-language surname of likely Iberian origin borne by various notable individuals.
- 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: Viva Fan Triple: [Viva Aerobus, loyaltyProgram, Viva Fan]
Generated description
Viva Fan is the frequent-flyer loyalty program of Mexican low-cost airline Viva Aerobus, offering members points, discounts, and travel-related benefits.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Viva Fan Target entity description: Viva Fan is the frequent-flyer loyalty program of Mexican low-cost airline Viva Aerobus, offering members points, discounts, and travel-related benefits.
-
A.
Viva
Viva is a bus rapid transit service in York Region, Ontario, Canada, providing frequent, limited-stop public transportation along major corridors.
-
B.
Viva
Viva is a German music television channel that gained popularity in the 1990s and 2000s for its music videos, pop culture programming, and youth-oriented shows.
-
C.
Toni Vega
Toni Vega is a musical artist known for contributing featured vocals to tracks such as "Monkey Business."
-
D.
Thalia
Thalia is one of the nine Muses in Greek mythology, traditionally associated with comedy and pastoral poetry.
-
E.
Vivanco
Vivanco is a Spanish-language surname of likely Iberian origin borne by various notable individuals.
- 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_69a88aa308a88190b0b86c011fda7fce |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc6337e948190bb4860f7045914e1 |
completed | March 7, 2026, 6:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae896b357c8190a6cdf99d5292037e |
completed | March 9, 2026, 8:48 a.m. |
| NEDg | Description generation | batch_69ae8e8263a08190a0950dbb1336df70 |
completed | March 9, 2026, 9:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae8f9fe79c819080062587aed27379 |
completed | March 9, 2026, 9:15 a.m. |
Created at: March 4, 2026, 7:49 p.m.