Triple
T16371307
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Let’s Get Lost |
E397569
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Stephen Holden
Stephen Holden is an American film and music critic best known for his long tenure at The New York Times.
|
E1222019
|
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: Stephen Holden | Statement: [Let’s Get Lost, hasCastMember, Stephen Holden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stephen Holden Context triple: [Let’s Get Lost, hasCastMember, Stephen Holden]
-
A.
Vincent Kartheiser
Vincent Kartheiser is an American actor best known for his role as ambitious ad executive Pete Campbell on the television series "Mad Men."
-
B.
Richard Gant
Richard Gant is an American character actor known for his roles in film and television, often portraying authoritative or tough-minded figures.
-
C.
Michael Lark
Michael Lark is an American comic book artist best known for his gritty, realistic artwork on series such as Gotham Central, Daredevil, and Lazarus.
-
D.
Gene Draper
Gene Draper is the infant son of Don and Betty Draper on the television series "Mad Men," named after Betty's late father.
-
E.
Lewis Pullman
Lewis Pullman is an American actor known for roles in films such as "Top Gun: Maverick," "Bad Times at the El Royale," and "The Strangers: Prey at Night."
- 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: Stephen Holden Triple: [Let’s Get Lost, hasCastMember, Stephen Holden]
Generated description
Stephen Holden is an American film and music critic best known for his long tenure at The New York Times.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stephen Holden Target entity description: Stephen Holden is an American film and music critic best known for his long tenure at The New York Times.
-
A.
Vincent Kartheiser
Vincent Kartheiser is an American actor best known for his role as ambitious ad executive Pete Campbell on the television series "Mad Men."
-
B.
Richard Gant
Richard Gant is an American character actor known for his roles in film and television, often portraying authoritative or tough-minded figures.
-
C.
Michael Lark
Michael Lark is an American comic book artist best known for his gritty, realistic artwork on series such as Gotham Central, Daredevil, and Lazarus.
-
D.
Gene Draper
Gene Draper is the infant son of Don and Betty Draper on the television series "Mad Men," named after Betty's late father.
-
E.
Lewis Pullman
Lewis Pullman is an American actor known for roles in films such as "Top Gun: Maverick," "Bad Times at the El Royale," and "The Strangers: Prey at Night."
- 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_69d87f2778dc8190aa95c7572db127e6 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2ff420d04819096ff12e08edf2f8b |
completed | April 18, 2026, 3:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a006ecdffac81908ca03a88974203f9 |
completed | May 10, 2026, 11:41 a.m. |
| NEDg | Description generation | batch_6a00710bbed08190a7c69312141a57b9 |
completed | May 10, 2026, 11:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00716c19088190aa511a0fce30bc83 |
completed | May 10, 2026, 11:52 a.m. |
Created at: April 10, 2026, 5:08 a.m.