Triple
T16501103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tony Snow |
E400800
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Snow |
E649273
|
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: [Tony Snow, familyName, Snow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Snow Context triple: [Tony 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 South Korean photo and video messaging app known for its augmented reality filters and stickers, similar in concept to Snapchat.
-
C.
Snow
"Snow" is a political and philosophical novel by Turkish Nobel laureate Orhan Pamuk that explores identity, secularism, and Islamism in contemporary Turkey.
-
D.
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.
-
E.
Snow
chosen
Snow is a common English surname borne by various notable figures in literature, science, and public life.
- 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_69d88381f6148190819958a038be990e |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e4ddc88819081fedb726add3e96 |
completed | April 18, 2026, 7:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00582e6e288190825af8758097e325 |
completed | May 10, 2026, 10:04 a.m. |
Created at: April 10, 2026, 5:14 a.m.