Triple
T17719227
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lisa Eilbacher |
E442286
|
entity |
| Predicate | portrayedCharacter |
P1668
|
FINISHED |
| Object |
Casey Seeger
Casey Seeger is a character from the film "An Officer and a Gentleman," known as one of the aspiring Navy officers training alongside the protagonist.
|
E1284479
|
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: Casey Seeger | Statement: [Lisa Eilbacher, portrayedCharacter, Casey Seeger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Casey Seeger Context triple: [Lisa Eilbacher, portrayedCharacter, Casey Seeger]
-
A.
Allyson Seeger
Allyson Seeger is a film producer known for her work on the 2022 dark comedy "The Estate."
-
B.
Casey Welson
Casey Welson is a teenage kidnapping victim whose abduction drives the suspenseful plot of the 2013 thriller film "The Call."
-
C.
Emily Sweeney
Emily Sweeney is a dermatologist who appears as Rajesh Koothrappali’s love interest on the television sitcom "The Big Bang Theory."
-
D.
Catherine Louise Fink
Catherine Louise Fink was the birth name of Kay Thompson, the American author, actress, singer, and creator of the "Eloise" children's books.
-
E.
Mitchell Alsup
Mitchell Alsup is a computer engineer best known as one of the founders of Transmeta, a company that developed innovative low-power microprocessor technologies.
- 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: Casey Seeger Triple: [Lisa Eilbacher, portrayedCharacter, Casey Seeger]
Generated description
Casey Seeger is a character from the film "An Officer and a Gentleman," known as one of the aspiring Navy officers training alongside the protagonist.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Casey Seeger Target entity description: Casey Seeger is a character from the film "An Officer and a Gentleman," known as one of the aspiring Navy officers training alongside the protagonist.
-
A.
Allyson Seeger
Allyson Seeger is a film producer known for her work on the 2022 dark comedy "The Estate."
-
B.
Casey Welson
Casey Welson is a teenage kidnapping victim whose abduction drives the suspenseful plot of the 2013 thriller film "The Call."
-
C.
Emily Sweeney
Emily Sweeney is a dermatologist who appears as Rajesh Koothrappali’s love interest on the television sitcom "The Big Bang Theory."
-
D.
Catherine Louise Fink
Catherine Louise Fink was the birth name of Kay Thompson, the American author, actress, singer, and creator of the "Eloise" children's books.
-
E.
Mitchell Alsup
Mitchell Alsup is a computer engineer best known as one of the founders of Transmeta, a company that developed innovative low-power microprocessor technologies.
- 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_69d8b9ec79688190b86bdcef85a7b3aa |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e474844ff48190b1cf181eb9c113d9 |
completed | April 19, 2026, 6:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a02302defe081909eaeb1f90d497561 |
completed | May 11, 2026, 7:38 p.m. |
| NEDg | Description generation | batch_6a0231835c748190b6908d6ddb5f25b4 |
completed | May 11, 2026, 7:44 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0232033f788190b0be2d3976ed57e4 |
completed | May 11, 2026, 7:46 p.m. |
Created at: April 10, 2026, 10:07 a.m.