Triple
T15928246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ka |
E386258
|
entity |
| Predicate | appearsIn |
P795
|
FINISHED |
| Object | Snow |
E1184897
|
NE FINISHED |
How this triple was built (2 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: [Ka, appearsIn, Snow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Snow Context triple: [Ka, appearsIn, 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 concept progressive rock double album by Spock’s Beard, known for its elaborate storytelling and complex musicianship.
-
D.
Snow
Snow is a South Korean photo and video messaging app known for its augmented reality filters and stickers, similar in concept to Snapchat.
-
E.
Snow
chosen
Snow is the fictional universe in which the manga and anime series "Ka" is set, encompassing its unique world, lore, and settings.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156872964819083a2fb9f86df61ba |
completed | April 16, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffbe727c348190907c9e7a5db6031d |
completed | May 9, 2026, 11:08 p.m. |
Created at: April 10, 2026, 4:52 a.m.