Triple
T10323387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Swimfan |
E242694
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Amy Miller
Amy Miller is the obsessive and dangerously fixated teenage girl who serves as the central antagonist in the psychological thriller film "Swimfan."
|
E860257
|
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 Miller | Statement: [Swimfan, mainCharacter, Amy Miller]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amy Miller Context triple: [Swimfan, mainCharacter, Amy Miller]
-
A.
Susan Miller
Susan Miller is a film producer best known for her work on the fantasy romantic comedy "Ella Enchanted."
-
B.
Colleen Miller
Colleen Miller is an American actress known for her film and television roles during the 1950s.
-
C.
Tricia Miller
Tricia Miller is a troubled but endearing young inmate in the television series "Orange Is the New Black," known for her close bond with fellow prisoner Poussey Washington.
-
D.
Suzy Miller
Suzy Miller is a former British model and socialite best known for her high-profile marriages to Formula One driver James Hunt and later actor Richard Burton.
-
E.
Anne Leahy
Anne Leahy is a Canadian diplomat and academic known for her service as ambassador to countries including Russia and the Holy See and for her expertise in international relations and religious diplomacy.
- 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 Miller Triple: [Swimfan, mainCharacter, Amy Miller]
Generated description
Amy Miller is the obsessive and dangerously fixated teenage girl who serves as the central antagonist in the psychological thriller film "Swimfan."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Amy Miller Target entity description: Amy Miller is the obsessive and dangerously fixated teenage girl who serves as the central antagonist in the psychological thriller film "Swimfan."
-
A.
Susan Miller
Susan Miller is a film producer best known for her work on the fantasy romantic comedy "Ella Enchanted."
-
B.
Colleen Miller
Colleen Miller is an American actress known for her film and television roles during the 1950s.
-
C.
Tricia Miller
Tricia Miller is a troubled but endearing young inmate in the television series "Orange Is the New Black," known for her close bond with fellow prisoner Poussey Washington.
-
D.
Suzy Miller
Suzy Miller is a former British model and socialite best known for her high-profile marriages to Formula One driver James Hunt and later actor Richard Burton.
-
E.
Anne Leahy
Anne Leahy is a Canadian diplomat and academic known for her service as ambassador to countries including Russia and the Holy See and for her expertise in international relations and religious diplomacy.
- 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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d6cdb6cc8190b37ca4494287128b |
completed | April 7, 2026, 10:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d794f3a130819095a8befd8fa3a9f0 |
completed | April 9, 2026, noon |
| NEDg | Description generation | batch_69d799e6ffec8190aa6bd9b82efba0d7 |
completed | April 9, 2026, 12:21 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d79af4c5e48190a0f36f689fa5208c |
completed | April 9, 2026, 12:26 p.m. |
Created at: April 6, 2026, 11:50 a.m.