Triple
T11447883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hell's Angels |
E271310
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object |
Helen
Helen is a character in the 1930 aviation war film "Hell's Angels," which is known for its groundbreaking aerial combat sequences and early use of sound in cinema.
|
E824714
|
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: Helen | Statement: [Hell's Angels, featuresCharacter, Helen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Helen Context triple: [Hell's Angels, featuresCharacter, Helen]
-
A.
Helen
Helen is a central survivor and maternal figure in the post-apocalyptic film "Waterworld," known for her determination to protect the child Enola and seek the mythical Dryland.
-
B.
Helen
Helen is the birth name of P. L. Travers, the Australian-British author best known for creating the "Mary Poppins" series.
-
C.
Helen
Helen is the given first name of New Zealand actress Pat Evison, known for her work in film, television, and theatre.
-
D.
Helen
Helen is the central character in the novel "The Spare Room," around whom the story’s emotional and narrative developments revolve.
-
E.
Helen
Helen is the given name of H. T. Lowe-Porter, the American translator best known for bringing Thomas Mann’s works into 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: Helen Triple: [Hell's Angels, featuresCharacter, Helen]
Generated description
Helen is a character in the 1930 aviation war film "Hell's Angels," which is known for its groundbreaking aerial combat sequences and early use of sound in cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Helen Target entity description: Helen is a character in the 1930 aviation war film "Hell's Angels," which is known for its groundbreaking aerial combat sequences and early use of sound in cinema.
-
A.
Helen
chosen
Helen is a fictional character from the 1930 aviation war film "Hell's Angels," which is renowned for its groundbreaking aerial combat sequences and early sound-era spectacle.
-
B.
Helen
Helen is the mute, terrorized heroine of the classic 1946 psychological thriller film "The Spiral Staircase."
-
C.
Helen
Helen is a character in Aldous Huxley’s novel "Eyeless in Gaza," representing one of the key figures in the book’s exploration of memory, morality, and personal transformation.
-
D.
Helen
Helen is the given first name of New Zealand actress Pat Evison, known for her work in film, television, and theatre.
-
E.
Helen
Helen is a fictional protagonist associated with a narrative set in or around New York City's Central Park.
- F. None of above.
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_69d6aadff8888190a13f253f0d460874 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d81c6d4890819082fb4a670feb2629 |
completed | April 9, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5d3cb63408190a96b97f716d46082 |
completed | April 20, 2026, 7:20 a.m. |
| NEDg | Description generation | batch_69e5d7e46e248190aba139dc32185e2f |
completed | April 20, 2026, 7:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e5e192297c8190992578f734e63427 |
completed | April 20, 2026, 8:19 a.m. |
Created at: April 8, 2026, 9:35 p.m.