Triple
T14854650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trainwreck |
E349319
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Amy Townsend
Amy Townsend is the hard-partying, commitment-averse magazine writer at the center of the romantic comedy film "Trainwreck."
|
E1149813
|
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: Amy Townsend | Statement: [Trainwreck, mainCharacter, Amy Townsend]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amy Townsend Context triple: [Trainwreck, mainCharacter, Amy Townsend]
-
A.
Lisa Townsend
Lisa Townsend is the elected Police and Crime Commissioner responsible for overseeing policing strategy and accountability in Surrey, England.
-
B.
Amy Dromey
Amy Dromey is the daughter of British Labour politician Harriet Harman and trade unionist Jack Dromey.
-
C.
Amy Ashwood
Amy Ashwood was a Jamaican Pan-Africanist, feminist, and co-founder of the Universal Negro Improvement Association alongside Marcus Garvey.
-
D.
Jill Townsend
Jill Townsend is an American former actress and journalist known for her work in film and television during the 1970s.
-
E.
Amy Robinson
Amy Robinson is an American actress and film producer best known for her breakout role in Martin Scorsese’s 1973 film "Mean Streets."
- 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: Amy Townsend Triple: [Trainwreck, mainCharacter, Amy Townsend]
Generated description
Amy Townsend is the hard-partying, commitment-averse magazine writer at the center of the romantic comedy film "Trainwreck."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Amy Townsend Target entity description: Amy Townsend is the hard-partying, commitment-averse magazine writer at the center of the romantic comedy film "Trainwreck."
-
A.
Lisa Townsend
Lisa Townsend is the elected Police and Crime Commissioner responsible for overseeing policing strategy and accountability in Surrey, England.
-
B.
Amy Dromey
Amy Dromey is the daughter of British Labour politician Harriet Harman and trade unionist Jack Dromey.
-
C.
Amy Ashwood
Amy Ashwood was a Jamaican Pan-Africanist, feminist, and co-founder of the Universal Negro Improvement Association alongside Marcus Garvey.
-
D.
Jill Townsend
Jill Townsend is an American former actress and journalist known for her work in film and television during the 1970s.
-
E.
Amy Robinson
Amy Robinson is an American actress and film producer best known for her breakout role in Martin Scorsese’s 1973 film "Mean Streets."
- 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_69d822ed7e1881909b90fca143ad7e34 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded44318f0819080b6c599f2d3474f |
completed | April 14, 2026, 11:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef885b07c8190af5e33303af9fbea |
completed | May 9, 2026, 9:04 a.m. |
| NEDg | Description generation | batch_69fefa54397c81909c9bfb8c0553b3d1 |
completed | May 9, 2026, 9:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fefb04d7e4819084ac10e05dccb3e3 |
completed | May 9, 2026, 9:14 a.m. |
Created at: April 10, 2026, 1:54 a.m.