Triple
T14423148
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naver Corporation |
E357631
|
entity |
| Predicate | operates |
P24
|
FINISHED |
| Object |
Snow
Snow is a South Korean photo and video messaging app known for its augmented reality filters and stickers, similar in concept to Snapchat.
|
E1099173
|
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: Snow | Statement: [Naver Corporation, operates, Snow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Snow Context triple: [Naver Corporation, operates, Snow]
-
A.
Snow
"Snow" is a political and philosophical novel by Turkish Nobel laureate Orhan Pamuk that explores identity, secularism, and Islamism in contemporary Turkey.
-
B.
Snow
Snow is a white color variant of the iMac G3, known for its clean, minimalist appearance among the line’s iconic translucent and colorful designs.
-
C.
Snow
"Snow" is a notable abstract painting by British artist Howard Hodgkin, recognized for its expressive brushwork and evocative use of color to suggest memory and atmosphere.
-
D.
Snow
"Snow" is a song featured on the album *Back to Scratch* by Welsh singer-songwriter Charlotte Church.
-
E.
Snow
Snow is a common English surname borne by various notable figures in literature, science, and public life.
- 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: Snow Triple: [Naver Corporation, operates, Snow]
Generated description
Snow is a South Korean photo and video messaging app known for its augmented reality filters and stickers, similar in concept to Snapchat.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Snow Target entity description: Snow is a South Korean photo and video messaging app known for its augmented reality filters and stickers, similar in concept to Snapchat.
-
A.
Snow
Snow is frozen atmospheric precipitation in the form of ice crystals that accumulate on the ground, often creating white, wintry landscapes.
-
B.
Snow
"Snow" is a festive song from the 1954 musical film *White Christmas*, celebrated for its nostalgic lyrics about the beauty and romance of wintertime snowfall.
-
C.
Snow
Snow is a white color variant of the iMac G3, known for its clean, minimalist appearance among the line’s iconic translucent and colorful designs.
-
D.
Snow
"Snow" is a political and philosophical novel by Turkish Nobel laureate Orhan Pamuk that explores identity, secularism, and Islamism in contemporary Turkey.
-
E.
Snow
Snow is a common English surname borne by various notable figures in literature, science, and public life.
- 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_69d82793421c8190861eb0e673b085de |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91102c3c81908f571a1fff3bdd47 |
completed | April 14, 2026, 7:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5bcd2a908190ad7d5ebf11b41551 |
completed | May 8, 2026, 3:43 a.m. |
| NEDg | Description generation | batch_69fd5d585cc08190908bc5f9b8abdb82 |
completed | May 8, 2026, 3:49 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd5e0bbd6c8190b14039b3335692c7 |
completed | May 8, 2026, 3:52 a.m. |
Created at: April 10, 2026, 1:18 a.m.