Triple
T11880146
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Applause (musical) |
E282636
|
entity |
| Predicate | producedBy |
P490
|
FINISHED |
| Object |
Lawrence Kasha
Lawrence Kasha was an American theatre producer and director best known for his work on Broadway musicals and adaptations.
|
E1111758
|
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: Lawrence Kasha | Statement: [Applause (musical), producedBy, Lawrence Kasha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lawrence Kasha Context triple: [Applause (musical), producedBy, Lawrence Kasha]
-
A.
Lawrence Kasanoff
Lawrence Kasanoff is an American film producer best known for his work on the Mortal Kombat franchise and various action and animated films.
-
B.
Howard Lasnik
Howard Lasnik is a prominent American linguist known for his influential work in generative syntax and his close collaboration with Noam Chomsky in developing contemporary syntactic theory.
-
C.
George Lerner
George Lerner was an American toy inventor best known for creating the iconic Mr. Potato Head character.
-
D.
Michael Vavitch
Michael Vavitch was a silent-era film actor known for his role in the 1924 drama "The Red Lily."
-
E.
Edward A. Garmatz
Edward A. Garmatz was a long-serving U.S. Congressman from Maryland who represented Baltimore in the House of Representatives in the mid-20th century.
- 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: Lawrence Kasha Triple: [Applause (musical), producedBy, Lawrence Kasha]
Generated description
Lawrence Kasha was an American theatre producer and director best known for his work on Broadway musicals and adaptations.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lawrence Kasha Target entity description: Lawrence Kasha was an American theatre producer and director best known for his work on Broadway musicals and adaptations.
-
A.
Lawrence Kasanoff
Lawrence Kasanoff is an American film producer best known for his work on the Mortal Kombat franchise and various action and animated films.
-
B.
Howard Lasnik
Howard Lasnik is a prominent American linguist known for his influential work in generative syntax and his close collaboration with Noam Chomsky in developing contemporary syntactic theory.
-
C.
George Lerner
George Lerner was an American toy inventor best known for creating the iconic Mr. Potato Head character.
-
D.
Michael Vavitch
Michael Vavitch was a silent-era film actor known for his role in the 1924 drama "The Red Lily."
-
E.
Edward A. Garmatz
Edward A. Garmatz was a long-serving U.S. Congressman from Maryland who represented Baltimore in the House of Representatives in the mid-20th century.
- 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_69d6ab2945d081908a5851c916cbcfb5 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8be1cad5c8190a45dfb0f0cc2a512 |
completed | April 10, 2026, 9:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdd5b2fb308190ab041a9cfe39087c |
completed | May 8, 2026, 12:23 p.m. |
| NEDg | Description generation | batch_69fdd75d410481909a33799507689f21 |
completed | May 8, 2026, 12:30 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fdd7ddc1d88190bc56b1603093a5db |
completed | May 8, 2026, 12:32 p.m. |
Created at: April 8, 2026, 9:44 p.m.