Triple
T3315335
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Silence |
E69667
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Tessa Berens
Tessa Berens is a fictional character from the work titled "The Silence."
|
E448259
|
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: Tessa Berens | Statement: [The Silence, hasCharacter, Tessa Berens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tessa Berens Context triple: [The Silence, hasCharacter, Tessa Berens]
-
A.
Tessa Ensler
Tessa Ensler is a character portrayed by Jodie Comer, likely in a dramatic screen or stage production.
-
B.
Tammara Draut
Tammara Draut is an American higher education leader who serves as president of the University of Indianapolis.
-
C.
Kate Dibiasky
Kate Dibiasky is a fictional astronomy PhD candidate from the film "Don't Look Up" who discovers a planet-killing comet and becomes a central figure in the effort to warn humanity.
-
D.
Natalie Schafer
Natalie Schafer was an American actress best known for playing the wealthy and daffy Lovey Howell on the classic television sitcom "Gilligan's Island."
-
E.
Lisa Eilbacher
Lisa Eilbacher is an American actress best known for her roles in 1980s films and television series, including prominent appearances in action and drama movies.
- 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: Tessa Berens Triple: [The Silence, hasCharacter, Tessa Berens]
Generated description
Tessa Berens is a fictional character from the work titled "The Silence."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tessa Berens Target entity description: Tessa Berens is a fictional character from the work titled "The Silence."
-
A.
Tessa Ensler
Tessa Ensler is a character portrayed by Jodie Comer, likely in a dramatic screen or stage production.
-
B.
Tammara Draut
Tammara Draut is an American higher education leader who serves as president of the University of Indianapolis.
-
C.
Kate Dibiasky
Kate Dibiasky is a fictional astronomy PhD candidate from the film "Don't Look Up" who discovers a planet-killing comet and becomes a central figure in the effort to warn humanity.
-
D.
Natalie Schafer
Natalie Schafer was an American actress best known for playing the wealthy and daffy Lovey Howell on the classic television sitcom "Gilligan's Island."
-
E.
Lisa Eilbacher
Lisa Eilbacher is an American actress best known for her roles in 1980s films and television series, including prominent appearances in action and drama movies.
- 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_69ad85a0bb048190a5458d2738012d61 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb110b28081909b366623e3b0783d |
completed | March 8, 2026, 5:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd7f501e588190b666141f7e5ed6ae |
completed | March 20, 2026, 5:09 p.m. |
| NEDg | Description generation | batch_69bd84bae7148190ae201ea5257dd43e |
completed | March 20, 2026, 5:32 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bd857181e4819086b7d0b493fbb9a3 |
completed | March 20, 2026, 5:35 p.m. |
Created at: March 8, 2026, 3:11 p.m.