Triple
T5279507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarah Connor |
E119456
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object |
Willa Taylor
Willa Taylor is an actress known for playing the character Sarah Connor.
|
E509267
|
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: Willa Taylor | Statement: [Sarah Connor, portrayedBy, Willa Taylor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Willa Taylor Context triple: [Sarah Connor, portrayedBy, Willa Taylor]
-
A.
Rachael Taylor
Rachael Taylor is an Australian actress known for her roles in films like "Transformers" and TV series such as "Jessica Jones."
-
B.
Lindsay Duncan
Lindsay Duncan is a Scottish actress acclaimed for her work on stage, film, and television, known for roles in productions such as "About Time," "Rome," and "Doctor Who."
-
C.
Sarah Sedgwick
Sarah Sedgwick was a colonial-era New England woman known primarily as the wife of Harvard-educated lawyer and Massachusetts governor John Leverett.
-
D.
Rebecca Garland
Rebecca Garland is one of the children of Merrick Garland, the U.S. Attorney General and former federal judge.
-
E.
Robin Tunney
Robin Tunney is an American actress known for her roles in films like "The Craft" and "Empire Records" and the TV series "The Mentalist."
- 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: Willa Taylor Triple: [Sarah Connor, portrayedBy, Willa Taylor]
Generated description
Willa Taylor is an actress known for playing the character Sarah Connor.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Willa Taylor Target entity description: Willa Taylor is an actress known for playing the character Sarah Connor.
-
A.
Rachael Taylor
Rachael Taylor is an Australian actress known for her roles in films like "Transformers" and TV series such as "Jessica Jones."
-
B.
Lindsay Duncan
Lindsay Duncan is a Scottish actress acclaimed for her work on stage, film, and television, known for roles in productions such as "About Time," "Rome," and "Doctor Who."
-
C.
Sarah Sedgwick
Sarah Sedgwick was a colonial-era New England woman known primarily as the wife of Harvard-educated lawyer and Massachusetts governor John Leverett.
-
D.
Rebecca Garland
Rebecca Garland is one of the children of Merrick Garland, the U.S. Attorney General and former federal judge.
-
E.
Robin Tunney
Robin Tunney is an American actress known for her roles in films like "The Craft" and "Empire Records" and the TV series "The Mentalist."
- 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_69bd446d05a8819092ad333a3f9c8d5c |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd84c409248190a0154a660f58e096 |
completed | March 20, 2026, 5:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf06dd56a08190a7cfef614e5990f4 |
completed | March 21, 2026, 9 p.m. |
| NEDg | Description generation | batch_69bf08eb8a50819092df2f12679fbca0 |
completed | March 21, 2026, 9:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf0cd1307481909a60298929af7699 |
completed | March 21, 2026, 9:25 p.m. |
Created at: March 20, 2026, 1:52 p.m.