Triple
T13645743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal Historians of Arendelle |
E326095
|
entity |
| Predicate | associatedWithCharacter |
P1481
|
FINISHED |
| Object |
Anna
Anna is a courageous and optimistic princess of Arendelle from Disney's Frozen franchise, known for her deep love for her sister Elsa and her adventurous spirit.
|
E198466
|
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: [Royal Historians of Arendelle, associatedWithCharacter, Anna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Context triple: [Royal Historians of Arendelle, associatedWithCharacter, 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: [Royal Historians of Arendelle, associatedWithCharacter, Anna]
Generated description
Anna is a courageous and optimistic princess of Arendelle from Disney's Frozen franchise, known for her deep love for her sister Elsa and her adventurous spirit.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anna Target entity description: Anna is a courageous and optimistic princess of Arendelle from Disney's Frozen franchise, known for her deep love for her sister Elsa and her adventurous spirit.
-
A.
Anna
chosen
Anna is a spirited and optimistic princess from Disney's animated film "Frozen," known for her bravery, loyalty, and deep love for her sister Elsa.
-
B.
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.
-
C.
Anna
Anna is a fictional character played by British actress Naomi Ackie, known for her work in film and television.
-
D.
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.
-
E.
Anna
Anna is a supporting character in Hector Berlioz’s grand opera *Les Troyens*, typically portrayed as Dido’s loyal sister and confidante.
- 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_69d8076beddc8190a53156f5bea77f5e |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc60635d08190899806fe8936f02a |
completed | April 12, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f78add2b0c8190ade1af991744c4e0 |
completed | May 3, 2026, 5:50 p.m. |
| NEDg | Description generation | batch_69f78c8d68f081909f5e6b8ab05a3ce2 |
completed | May 3, 2026, 5:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f78d6d74bc8190ad5476a06e8fd8ad |
completed | May 3, 2026, 6:01 p.m. |
Created at: April 9, 2026, 9:51 p.m.