Triple
T5590764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Halloweentown |
E146870
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Paul Bernbaum
Paul Bernbaum is an American screenwriter best known for writing the Disney Channel fantasy film "Halloweentown."
|
E607844
|
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: Paul Bernbaum | Statement: [Halloweentown, screenwriter, Paul Bernbaum]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paul Bernbaum Context triple: [Halloweentown, screenwriter, Paul Bernbaum]
-
A.
Peter Rosenbaum
Peter Rosenbaum is a cinematographer best known for his work on the 2008 coming-of-age film "The Wackness."
-
B.
Paul Feldman
Paul Feldman is a computer scientist and cryptographer known for his work on digital signatures and other foundational topics in modern cryptography.
-
C.
Philip Brenner
Philip Brenner is a scholar and author known for his work on U.S. foreign policy and Latin American studies, often collaborating with historian James G. Blight.
-
D.
Paul Preuss
Paul Preuss is a science fiction author known for his hard-SF novels and collaborations, including work with Arthur C. Clarke.
-
E.
Michael Berman
Michael Berman is a writer and contributor known for his work published in George magazine.
- 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: Paul Bernbaum Triple: [Halloweentown, screenwriter, Paul Bernbaum]
Generated description
Paul Bernbaum is an American screenwriter best known for writing the Disney Channel fantasy film "Halloweentown."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Paul Bernbaum Target entity description: Paul Bernbaum is an American screenwriter best known for writing the Disney Channel fantasy film "Halloweentown."
-
A.
Peter Rosenbaum
Peter Rosenbaum is a cinematographer best known for his work on the 2008 coming-of-age film "The Wackness."
-
B.
Paul Feldman
Paul Feldman is a computer scientist and cryptographer known for his work on digital signatures and other foundational topics in modern cryptography.
-
C.
Philip Brenner
Philip Brenner is a scholar and author known for his work on U.S. foreign policy and Latin American studies, often collaborating with historian James G. Blight.
-
D.
Paul Preuss
Paul Preuss is a science fiction author known for his hard-SF novels and collaborations, including work with Arthur C. Clarke.
-
E.
Michael Berman
Michael Berman is a writer and contributor known for his work published in George magazine.
- 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_69c009036c408190981a8d690b679b67 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020a1d4cc8190a52264dfba6aa011 |
completed | March 22, 2026, 5:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6e3ff314c8190b66f8b0a7ae3c039 |
completed | March 27, 2026, 8:09 p.m. |
| NEDg | Description generation | batch_69c6e7c75140819082a32e4662e0b07c |
completed | March 27, 2026, 8:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6e8881b848190bd6184aeaf311d24 |
completed | March 27, 2026, 8:28 p.m. |
Created at: March 22, 2026, 3:38 p.m.