Triple
T16411854
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thierry Arbogast |
E398585
|
entity |
| Predicate | workedOn |
P3
|
FINISHED |
| Object |
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.
|
E1212740
|
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: [Thierry Arbogast, workedOn, Anna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Context triple: [Thierry Arbogast, workedOn, 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 an actress known for portraying the ambitious and manipulative Lady Macbeth in a production of Shakespeare’s tragedy "Macbeth."
-
C.
Anna
Anna is a biblical figure in the Book of Tobit, known as Tobit's wife and the mother of Tobias.
-
D.
Anna
Anna is a woman whose full name is Mrs. Anna Smith.
-
E.
Anna
Anna of Moscow was a medieval Russian noblewoman and princess associated with the ruling dynasties of Muscovy.
- 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: [Thierry Arbogast, workedOn, Anna]
Generated description
"Anna" is a 2019 action-thriller film written and directed by Luc Besson, centered on a highly skilled female assassin leading a double life.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anna Target entity description: "Anna" is a 2019 action-thriller film written and directed by Luc Besson, centered on a highly skilled female assassin leading a double life.
-
A.
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.
-
B.
Anna
Anna is a central female character in the comedy Western film "A Million Ways to Die in the West," portrayed as a sharp-shooting, quick-witted woman who helps the protagonist toughen up in the dangerous frontier.
-
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 the given first name of Anny Ondra, the Czech-Austrian film actress best known for her work in early European cinema and Alfred Hitchcock’s films.
-
E.
Anna
Anna is a fictional character played by British actress Naomi Ackie, known for her work in film and television.
- 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_69d87f2950248190bc8ad9b9bebdc8c8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32874a0cc8190874aea10b1d13004 |
completed | April 18, 2026, 6:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00457d9f4081908a5f28eeafc44695 |
completed | May 10, 2026, 8:44 a.m. |
| NEDg | Description generation | batch_6a00469f5d8081908c21fc30dcb5d5b8 |
completed | May 10, 2026, 8:49 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0047322384819082a95f9a6f5a53fc |
completed | May 10, 2026, 8:52 a.m. |
Created at: April 10, 2026, 5:09 a.m.