Triple
T18661727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brent Jennings |
E456213
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Snowfall |
—
|
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: Snowfall | Statement: [Brent Jennings, notableWork, Snowfall]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Snowfall Context triple: [Brent Jennings, notableWork, Snowfall]
-
A.
Snowfall
"Snowfall" is a song by the indie rock band The Halo Benders, known for their offbeat, lo-fi sound and Doug Martsch’s distinctive vocals.
-
B.
Snowfall
chosen
Snowfall is an American crime drama television series that explores the early days of the crack cocaine epidemic in 1980s Los Angeles.
-
C.
Snowdown
Snowdown is a former coal mining village in the Kent coalfield of southeast England, historically centered around Snowdown Colliery and its mining community.
-
D.
First Snow
First Snow is a 2006 psychological thriller film about a salesman whose life unravels after a fortune teller predicts his imminent death.
-
E.
Flying Snow
Flying Snow is a skilled and passionate female assassin in the Chinese wuxia film "Hero," whose tragic love and fierce loyalty drive much of the story’s emotional conflict.
- 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_69d8d38f72b4819090a935175d9ca8af |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5508b35ec819085c1c4c2c98d6672 |
completed | April 19, 2026, 10 p.m. |
Created at: April 10, 2026, 11:48 a.m.