Triple
T17332291
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Possession (1981 film) |
E420845
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Anna
Anna is the psychologically tormented protagonist of the 1981 horror film "Possession," whose unraveling marriage and disturbing behavior drive the movie’s surreal and unsettling narrative.
|
E1262639
|
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: Anna | Statement: [Possession (1981 film), mainCharacter, Anna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Context triple: [Possession (1981 film), mainCharacter, Anna]
-
A.
Anna
Anna is the given name of Anna Murray Douglass, an African American abolitionist and the first wife of Frederick Douglass.
-
B.
Anna
Anna is a key female resistance fighter in the World War II adventure film "The Guns of Navarone," whose complex loyalties and actions significantly impact the mission’s outcome.
-
C.
Anna
Anna is a small city in north-central Texas that forms part of the fast-growing suburban region north of Dallas.
-
D.
Anna
Anna is a biblical figure in the Book of Tobit, known as Tobit's wife and the mother of Tobias.
-
E.
Anna
Anna is the given first name of the American actress, comedian, and director Nancy Walker.
- 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: Anna Triple: [Possession (1981 film), mainCharacter, Anna]
Generated description
Anna is the psychologically tormented protagonist of the 1981 horror film "Possession," whose unraveling marriage and disturbing behavior drive the movie’s surreal and unsettling narrative.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anna Target entity description: Anna is the psychologically tormented protagonist of the 1981 horror film "Possession," whose unraveling marriage and disturbing behavior drive the movie’s surreal and unsettling narrative.
-
A.
Anna
Anna is the central female protagonist of the Italian film "Yesterday, Today and Tomorrow," whose intertwined romantic and comedic experiences drive one of the movie’s key narrative segments.
-
B.
Anna
"Anna" is a 2019 action-thriller film written and directed by Luc Besson, centered on a highly skilled female assassin leading a double life.
-
C.
Anna
Anna is a character from the "Predator" franchise, appearing as one of the human figures caught up in the deadly encounters with the extraterrestrial hunter.
-
D.
Anna
Anna is a fictional character played by British actress Naomi Ackie, known for her work in film and television.
-
E.
Anna
Anna is a central character in Harold Pinter’s play "Old Times," embodying themes of memory, ambiguity, and the shifting nature of personal relationships.
- 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_69d889d3adc881909319f1edb8d2a956 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a0fa57881909071fd395b3d46c4 |
completed | April 19, 2026, 2:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a018c3e50ac8190bcbe7f2b77bee4b7 |
completed | May 11, 2026, 7:58 a.m. |
| NEDg | Description generation | batch_6a018ea07b38819089b6fa9d843cf0e1 |
completed | May 11, 2026, 8:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a018f3bd4948190b41f765e43dc0586 |
completed | May 11, 2026, 8:11 a.m. |
Created at: April 10, 2026, 5:43 a.m.