Triple
T12295168
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Fergus |
E293064
|
entity |
| Predicate | coWrote |
P7732
|
FINISHED |
| Object |
First Snow
First Snow is a 2006 psychological thriller film about a salesman whose life unravels after a fortune teller predicts his imminent death.
|
E975310
|
NE FINISHED |
How this triple was built (4 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: First Snow | Statement: [Mark Fergus, coWrote, First Snow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: First Snow Context triple: [Mark Fergus, coWrote, First Snow]
-
A.
Snowfall
Snowfall is an American crime drama television series that explores the early days of the crack cocaine epidemic in 1980s Los Angeles.
-
B.
Snow Wonder
Snow Wonder is a 2005 made-for-television holiday drama film that intertwines multiple characters' lives during a Christmas Eve snowstorm.
-
C.
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.
-
D.
Rooftops in the Snow
"Rooftops in the Snow" is an 1878 Impressionist painting by Gustave Caillebotte depicting Parisian rooftops blanketed in snow with a strikingly modern, atmospheric realism.
-
E.
White as Snow
"White as Snow" is a reflective, spiritually themed song by the Irish rock band U2 from their album "No Line on the Horizon."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: First Snow Triple: [Mark Fergus, coWrote, First Snow]
Generated description
First Snow is a 2006 psychological thriller film about a salesman whose life unravels after a fortune teller predicts his imminent death.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: First Snow Target entity description: First Snow is a 2006 psychological thriller film about a salesman whose life unravels after a fortune teller predicts his imminent death.
-
A.
Snowfall
Snowfall is an American crime drama television series that explores the early days of the crack cocaine epidemic in 1980s Los Angeles.
-
B.
Snow Wonder
Snow Wonder is a 2005 made-for-television holiday drama film that intertwines multiple characters' lives during a Christmas Eve snowstorm.
-
C.
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.
-
D.
Rooftops in the Snow
"Rooftops in the Snow" is an 1878 Impressionist painting by Gustave Caillebotte depicting Parisian rooftops blanketed in snow with a strikingly modern, atmospheric realism.
-
E.
White as Snow
"White as Snow" is a reflective, spiritually themed song by the Irish rock band U2 from their album "No Line on the Horizon."
- F. None of above. chosen
Provenance (5 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_69d6ab690ad081908c0ed3870ec82d53 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93ed7251c8190b94d7cd75ad49b9c |
completed | April 10, 2026, 6:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e79bf548190bf7f314222ed1ed1 |
completed | May 2, 2026, 3:55 p.m. |
| NEDg | Description generation | batch_69f62260d6708190808e52935a27e2c1 |
completed | May 2, 2026, 4:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6230f4c8081908a759efa43b4800b |
completed | May 2, 2026, 4:15 p.m. |
Created at: April 8, 2026, 9:52 p.m.