Triple
T20929268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Timeline (film) |
E515424
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Kate Ericson
Kate Ericson is the protagonist of the film "Timeline," around whom the story’s central events and character developments revolve.
|
E1457370
|
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: Kate Ericson | Statement: [Timeline (film), mainCharacter, Kate Ericson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kate Ericson Context triple: [Timeline (film), mainCharacter, Kate Ericson]
-
A.
Rachel Lapp
Rachel Lapp is a young Amish widow and mother who becomes entangled with a big-city detective in the 1985 crime thriller film "Witness."
-
B.
Susan Larson
Susan Larson is known as the wife of English actor and singer George Sanders.
-
C.
Emily Jenkins
Emily Jenkins is an American author best known for her children's books and young adult fiction, often written under the pen name E. Lockhart.
-
D.
Rebecca Giblin
Rebecca Giblin is an Australian legal scholar and advocate specializing in copyright, technology, and creators’ rights, known for her work on how digital platforms affect cultural industries.
-
E.
Carol Willis
Carol Willis is an architectural historian and curator best known as the founder and director of New York City's Skyscraper Museum, focusing on the history and design of tall buildings.
- 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: Kate Ericson Triple: [Timeline (film), mainCharacter, Kate Ericson]
Generated description
Kate Ericson is the protagonist of the film "Timeline," around whom the story’s central events and character developments revolve.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kate Ericson Target entity description: Kate Ericson is the protagonist of the film "Timeline," around whom the story’s central events and character developments revolve.
-
A.
Rachel Lapp
Rachel Lapp is a young Amish widow and mother who becomes entangled with a big-city detective in the 1985 crime thriller film "Witness."
-
B.
Susan Larson
Susan Larson is known as the wife of English actor and singer George Sanders.
-
C.
Emily Jenkins
Emily Jenkins is an American author best known for her children's books and young adult fiction, often written under the pen name E. Lockhart.
-
D.
Rebecca Giblin
Rebecca Giblin is an Australian legal scholar and advocate specializing in copyright, technology, and creators’ rights, known for her work on how digital platforms affect cultural industries.
-
E.
Carol Willis
Carol Willis is an architectural historian and curator best known as the founder and director of New York City's Skyscraper Museum, focusing on the history and design of tall buildings.
- 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_69e0b4fb431c8190b9d40e6a72f0cc87 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6f6545f8c81908a8c2f0e8d7b060e |
completed | April 21, 2026, 4 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a091fce6b148190b2dcf07d95696744 |
completed | May 17, 2026, 1:54 a.m. |
| NEDg | Description generation | batch_6a09206824148190881bef138312d37f |
completed | May 17, 2026, 1:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0920d752888190bffcfa7ce75cd2ad |
completed | May 17, 2026, 1:58 a.m. |
Created at: April 16, 2026, 12:49 p.m.