Triple
T8766624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teaching Mrs. Tingle |
E208353
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Stephen Semel
Stephen Semel is an American film and television editor known for his work on various feature films and TV series.
|
E757316
|
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 Semel | Statement: [Teaching Mrs. Tingle, editedBy, Stephen Semel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stephen Semel Context triple: [Teaching Mrs. Tingle, editedBy, Stephen Semel]
-
A.
Brad Segal
Brad Segal is a composer and musician best known for creating film scores, including the soundtrack for the teen comedy "Easy A."
-
B.
Todd Lieberman
Todd Lieberman is an American film producer known for his work on acclaimed movies such as "The Fighter" and other major Hollywood productions.
-
C.
Greg Shapiro
Greg Shapiro is an American film producer best known for his Academy Award-winning work on "The Hurt Locker" and other notable independent and studio films.
-
D.
Jeremy Kleiner
Jeremy Kleiner is an American film producer known for his work on acclaimed films such as the civil rights drama "Selma."
-
E.
Andrew Weisblum
Andrew Weisblum is an American film editor known for his work on major feature films, including collaborations with directors like Darren Aronofsky and Wes Anderson.
- 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 Semel Triple: [Teaching Mrs. Tingle, editedBy, Stephen Semel]
Generated description
Stephen Semel is an American film and television editor known for his work on various feature films and TV series.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stephen Semel Target entity description: Stephen Semel is an American film and television editor known for his work on various feature films and TV series.
-
A.
Brad Segal
Brad Segal is a composer and musician best known for creating film scores, including the soundtrack for the teen comedy "Easy A."
-
B.
Todd Lieberman
Todd Lieberman is an American film producer known for his work on acclaimed movies such as "The Fighter" and other major Hollywood productions.
-
C.
Greg Shapiro
Greg Shapiro is an American film producer best known for his Academy Award-winning work on "The Hurt Locker" and other notable independent and studio films.
-
D.
Jeremy Kleiner
Jeremy Kleiner is an American film producer known for his work on acclaimed films such as the civil rights drama "Selma."
-
E.
Andrew Weisblum
Andrew Weisblum is an American film editor known for his work on major feature films, including collaborations with directors like Darren Aronofsky and Wes Anderson.
- 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_69ca835df7e08190ac875664cca8f9ca |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5ee97fd0819087ef8fe14b37ae43 |
completed | March 31, 2026, 11:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf519f029081908ad0ae79f35b3e9d |
completed | April 3, 2026, 5:35 a.m. |
| NEDg | Description generation | batch_69cf560021148190b60f3f32b0a952b4 |
completed | April 3, 2026, 5:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cf5654aa4c8190a368a0caca8ed45b |
completed | April 3, 2026, 5:55 a.m. |
Created at: March 30, 2026, 6:41 p.m.