Triple
T12356272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Times |
E294620
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
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.
|
E993453
|
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: [Old Times, hasCharacter, Anna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Context triple: [Old Times, hasCharacter, Anna]
-
A.
Anna
Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
-
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 an actress known for portraying the ambitious and manipulative Lady Macbeth in a production of Shakespeare’s tragedy "Macbeth."
-
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 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: [Old Times, hasCharacter, Anna]
Generated description
Anna is a central character in Harold Pinter’s play "Old Times," embodying themes of memory, ambiguity, and the shifting nature of personal relationships.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anna Target entity description: Anna is a central character in Harold Pinter’s play "Old Times," embodying themes of memory, ambiguity, and the shifting nature of personal relationships.
-
A.
Anna
Anna is an actress known for portraying the ambitious and manipulative Lady Macbeth in a production of Shakespeare’s tragedy "Macbeth."
-
B.
Anna
Anna is a central fictional character in Michael Ondaatje's novel "Divisadero," around whom much of the story's emotional and narrative complexity revolves.
-
C.
Anna
Anna is the tragic, aristocratic heroine of Leo Tolstoy’s novel "Anna Karenina," whose passionate affair and struggle against societal norms lead to her downfall.
-
D.
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.
-
E.
Anna
Anna is a character in Giacomo Puccini's early opera-ballet *Le Villi*, which blends elements of romance and the supernatural.
- 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_69d6ab6ccbec8190b09e2d357aa80064 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f8e64dc81908c2242c68cd1b86e |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ea0a4c4819091a7a66c3b73d776 |
completed | May 2, 2026, 8:29 p.m. |
| NEDg | Description generation | batch_69f65fadc97081908376913e390cfc3d |
completed | May 2, 2026, 8:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f660c3d914819097b57784889ca389 |
completed | May 2, 2026, 8:38 p.m. |
Created at: April 8, 2026, 9:54 p.m.