Triple
T12898258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nok Air |
E308548
|
entity |
| Predicate | loyaltyProgram |
P178
|
FINISHED |
| Object |
Nok Fan Club
Nok Fan Club is the frequent-flyer loyalty program of Thai low-cost airline Nok Air, offering members points, benefits, and rewards for their travel.
|
E1008050
|
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: Nok Fan Club | Statement: [Nok Air, loyaltyProgram, Nok Fan Club]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nok Fan Club Context triple: [Nok Air, loyaltyProgram, Nok Fan Club]
-
A.
Nanikai
Nanikai is a locality within South Tarawa, the densely populated capital area of Kiribati in the central Pacific Ocean.
-
B.
Nancun Wanbo
Nancun Wanbo is a metro station in Guangzhou, China, serving the city’s Panyu District as part of its urban rapid transit network.
-
C.
Honancho
Honancho is a neighborhood in Tokyo, Japan, known as a residential area with convenient access to central city districts via the Tokyo Metro Marunouchi Line.
-
D.
Nanu Nanu
Nanu Nanu is the quirky alien greeting popularized by Robin Williams’s character Mork on the TV sitcom "Mork & Mindy."
-
E.
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.
- 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: Nok Fan Club Triple: [Nok Air, loyaltyProgram, Nok Fan Club]
Generated description
Nok Fan Club is the frequent-flyer loyalty program of Thai low-cost airline Nok Air, offering members points, benefits, and rewards for their travel.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nok Fan Club Target entity description: Nok Fan Club is the frequent-flyer loyalty program of Thai low-cost airline Nok Air, offering members points, benefits, and rewards for their travel.
-
A.
Nanikai
Nanikai is a locality within South Tarawa, the densely populated capital area of Kiribati in the central Pacific Ocean.
-
B.
Nancun Wanbo
Nancun Wanbo is a metro station in Guangzhou, China, serving the city’s Panyu District as part of its urban rapid transit network.
-
C.
Honancho
Honancho is a neighborhood in Tokyo, Japan, known as a residential area with convenient access to central city districts via the Tokyo Metro Marunouchi Line.
-
D.
Nanu Nanu
Nanu Nanu is the quirky alien greeting popularized by Robin Williams’s character Mork on the TV sitcom "Mork & Mindy."
-
E.
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.
- 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9717f3fc48190b61c8f6f36cd0725 |
completed | April 10, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a55f98c08190b8910b1443841fa7 |
completed | May 3, 2026, 1:31 a.m. |
| NEDg | Description generation | batch_69f6a6179cdc8190976daa1384032445 |
completed | May 3, 2026, 1:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6a6cbec348190a96a0194b2d6be4b |
completed | May 3, 2026, 1:37 a.m. |
Created at: April 9, 2026, 5:40 p.m.