Triple
T3083336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sophie von Haselberg |
E64308
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Love & Vodka
Love & Vodka is a film associated with actress Sophie von Haselberg, known as one of her notable screen roles.
|
E325068
|
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: Love & Vodka | Statement: [Sophie von Haselberg, notableWork, Love & Vodka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Love & Vodka Context triple: [Sophie von Haselberg, notableWork, Love & Vodka]
-
A.
Mad Love
"Mad Love" is a 1935 psychological horror film starring Peter Lorre (credited as László Löwenstein), known for its macabre tale of obsession and surgical mutilation.
-
B.
Hold My Liquor
"Hold My Liquor" is a moody, atmospheric hip-hop track by Kanye West featuring introspective lyrics and experimental production.
-
C.
Blood, Sex and Booze
Blood, Sex and Booze is a punk rock song by the American band Green Day, known for its raw energy and provocative themes.
-
D.
The Bottom of the Bottle
The Bottom of the Bottle is a 1956 American drama film directed by Henry Hathaway, based on Georges Simenon's novel about a lawyer whose escaped-convict brother seeks his help to cross the Mexican border.
-
E.
Margarita
Margarita is a feminine given name of Spanish origin, equivalent to "Margaret" in English.
- 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: Love & Vodka Triple: [Sophie von Haselberg, notableWork, Love & Vodka]
Generated description
Love & Vodka is a film associated with actress Sophie von Haselberg, known as one of her notable screen roles.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Love & Vodka Target entity description: Love & Vodka is a film associated with actress Sophie von Haselberg, known as one of her notable screen roles.
-
A.
Mad Love
"Mad Love" is a 1935 psychological horror film starring Peter Lorre (credited as László Löwenstein), known for its macabre tale of obsession and surgical mutilation.
-
B.
Hold My Liquor
"Hold My Liquor" is a moody, atmospheric hip-hop track by Kanye West featuring introspective lyrics and experimental production.
-
C.
Blood, Sex and Booze
Blood, Sex and Booze is a punk rock song by the American band Green Day, known for its raw energy and provocative themes.
-
D.
The Bottom of the Bottle
The Bottom of the Bottle is a 1956 American drama film directed by Henry Hathaway, based on Georges Simenon's novel about a lawyer whose escaped-convict brother seeks his help to cross the Mexican border.
-
E.
Margarita
Margarita is a feminine given name of Spanish origin, equivalent to "Margaret" in English.
- 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_69ad857bb4c88190a4cf27893fcabed8 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada1e877008190aacbd6f1357bdb9b |
completed | March 8, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1f89b650c8190983a00e37a42a794 |
completed | March 11, 2026, 11:19 p.m. |
| NEDg | Description generation | batch_69b1f992a8ec8190b3e37dddd93ac57b |
completed | March 11, 2026, 11:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b1f9f759408190a4f2121078fe13cb |
completed | March 11, 2026, 11:25 p.m. |
Created at: March 8, 2026, 3:03 p.m.