Triple
T13596641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sergio Peris-Mencheta |
E324838
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Snowfall |
E345561
|
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: Snowfall | Statement: [Sergio Peris-Mencheta, notableWork, Snowfall]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Snowfall Context triple: [Sergio Peris-Mencheta, notableWork, Snowfall]
-
A.
Snowfall
chosen
Snowfall is an American crime drama television series that explores the early days of the crack cocaine epidemic in 1980s Los Angeles.
-
B.
Snowdown
Snowdown is a former coal mining village in the Kent coalfield of southeast England, historically centered around Snowdown Colliery and its mining community.
-
C.
First Snow
First Snow is a 2006 psychological thriller film about a salesman whose life unravels after a fortune teller predicts his imminent death.
-
D.
Thunder Snow
Thunder Snow is a prominent Irish-bred Thoroughbred racehorse best known for winning back-to-back Dubai World Cups in 2018 and 2019.
-
E.
Snow Wonder
Snow Wonder is a 2005 made-for-television holiday drama film that intertwines multiple characters' lives during a Christmas Eve snowstorm.
- 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_69d80769eaf081909d82f44e484d6113 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb0590558819080ccc5874a650b1e |
completed | April 12, 2026, 2:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f76bc99dac8190bc267fdf405e8d58 |
completed | May 3, 2026, 3:37 p.m. |
Created at: April 9, 2026, 9:49 p.m.