Triple
T3196797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jodie Comer |
E66953
|
entity |
| Predicate | hasRole |
P161
|
FINISHED |
| Object |
Tessa Ensler
Tessa Ensler is a character portrayed by Jodie Comer, likely in a dramatic screen or stage production.
|
E442709
|
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 Ensler | Statement: [Jodie Comer, hasRole, Tessa Ensler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tessa Ensler Context triple: [Jodie Comer, hasRole, Tessa Ensler]
-
A.
Leah Salisbury
Leah Salisbury was the wife of American playwright and screenwriter Sidney Howard.
-
B.
Annalee Whitmore
Annalee Whitmore is a screenwriter known for her work on the classic musical film "Babes in Arms."
-
C.
Annalee Newman
Annalee Newman was the wife of influential American abstract expressionist painter Barnett Newman and an important steward of his artistic legacy.
-
D.
Alanna Ubach
Alanna Ubach is an American actress and voice actress known for her versatile character roles in film, television, and animation, including work in projects like "Legally Blonde," "Euphoria," and various animated features.
-
E.
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.
- 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 Ensler Triple: [Jodie Comer, hasRole, Tessa Ensler]
Generated description
Tessa Ensler is a character portrayed by Jodie Comer, likely in a dramatic screen or stage production.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tessa Ensler Target entity description: Tessa Ensler is a character portrayed by Jodie Comer, likely in a dramatic screen or stage production.
-
A.
Leah Salisbury
Leah Salisbury was the wife of American playwright and screenwriter Sidney Howard.
-
B.
Annalee Whitmore
Annalee Whitmore is a screenwriter known for her work on the classic musical film "Babes in Arms."
-
C.
Annalee Newman
Annalee Newman was the wife of influential American abstract expressionist painter Barnett Newman and an important steward of his artistic legacy.
-
D.
Alanna Ubach
Alanna Ubach is an American actress and voice actress known for her versatile character roles in film, television, and animation, including work in projects like "Legally Blonde," "Euphoria," and various animated features.
-
E.
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.
- 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_69ad8588ba18819086a10951c32ecb80 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada7177b488190b7a1b40ff3fae15f |
completed | March 8, 2026, 4:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b62788456c8190a484826e5d915d9a |
completed | March 15, 2026, 3:29 a.m. |
| NEDg | Description generation | batch_69b628fe10908190978dd0361628f54f |
completed | March 15, 2026, 3:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b629ab52c881909f7fbef6f77b5bc4 |
completed | March 15, 2026, 3:38 a.m. |
Created at: March 8, 2026, 3:07 p.m.