Triple
T13549990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Widows |
E323616
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Bella O’Reilly
Bella O’Reilly is a central fictional character in the crime drama film "Widows," involved in a high-stakes heist after her partner’s death.
|
E1047257
|
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: Bella O’Reilly | Statement: [Widows, mainCharacter, Bella O’Reilly]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bella O’Reilly Context triple: [Widows, mainCharacter, Bella O’Reilly]
-
A.
Bella Greene
Bella Greene is a relatively obscure individual whose specific public achievements or background are not widely documented.
-
B.
Bella Thornton
Bella Thornton is the daughter of American actor, filmmaker, and musician Billy Bob Thornton.
-
C.
Bella Baxter
Bella Baxter is the unconventional, resurrected heroine of Alasdair Gray’s novel and its film adaptation "Poor Things," known for her childlike mind in an adult body and her radical journey of self-discovery and liberation.
-
D.
Bella Higginbotham
Bella Higginbotham is an American actress best known for her role in the film "Troop Zero" and for appearing in various television and streaming series.
-
E.
Esme Regan
Esme Regan is the daughter of British actor and director Vincent Regan.
- 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: Bella O’Reilly Triple: [Widows, mainCharacter, Bella O’Reilly]
Generated description
Bella O’Reilly is a central fictional character in the crime drama film "Widows," involved in a high-stakes heist after her partner’s death.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bella O’Reilly Target entity description: Bella O’Reilly is a central fictional character in the crime drama film "Widows," involved in a high-stakes heist after her partner’s death.
-
A.
Bella Greene
Bella Greene is a relatively obscure individual whose specific public achievements or background are not widely documented.
-
B.
Bella Thornton
Bella Thornton is the daughter of American actor, filmmaker, and musician Billy Bob Thornton.
-
C.
Bella Baxter
Bella Baxter is the unconventional, resurrected heroine of Alasdair Gray’s novel and its film adaptation "Poor Things," known for her childlike mind in an adult body and her radical journey of self-discovery and liberation.
-
D.
Bella Higginbotham
Bella Higginbotham is an American actress best known for her role in the film "Troop Zero" and for appearing in various television and streaming series.
-
E.
Esme Regan
Esme Regan is the daughter of British actor and director Vincent Regan.
- 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafdcecf481909999a173b32a58cd |
completed | April 12, 2026, 2:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75da4e19c819090d649b60a2dd410 |
completed | May 3, 2026, 2:37 p.m. |
| NEDg | Description generation | batch_69f75ec5101081909652b0c0998b36c8 |
completed | May 3, 2026, 2:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f75f4a3b0c81908c0ca0351771953b |
completed | May 3, 2026, 2:44 p.m. |
Created at: April 9, 2026, 9:46 p.m.