Triple
T21595347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lorenzo Snow |
E532884
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Snow |
—
|
NE NERFINISHED |
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: [Lorenzo Snow, familyName, Snow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Snow Context triple: [Lorenzo Snow, familyName, Snow]
-
A.
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.
-
B.
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.
-
C.
Snow
"Snow" is a song featured on the album *Back to Scratch* by Welsh singer-songwriter Charlotte Church.
-
D.
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.
-
E.
Snow
chosen
Snow is frozen atmospheric precipitation in the form of ice crystals that accumulate on the ground, often creating white, wintry landscapes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c46251648190876f0427cf2d321b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eefae07e388190baf1d67852c7e5db |
completed | April 27, 2026, 5:57 a.m. |
Created at: April 16, 2026, 6:32 p.m.