Triple
T9722640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Howard Sibshaw |
E235514
|
entity |
| Predicate | originOfCharacter |
P90667
|
FINISHED |
| Object | British television comedy writing |
—
|
LITERAL FINISHED |
How this triple was built (2 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: British television comedy writing | Statement: [Howard Sibshaw, originOfCharacter, British television comedy writing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originOfCharacter Context triple: [Howard Sibshaw, originOfCharacter, British television comedy writing]
-
A.
characterOrigin
Indicates the source, background, or initial context from which a character originates.
-
B.
protagonistOrigin
Indicates that one entity is the origin, source, or starting point of the protagonist in a narrative or story.
-
C.
creatorOfCharacter
Indicates that one entity is the originator or author who created or conceived the other entity as a character.
-
D.
fictionalOrigin
Indicates that one entity originates from, or was first introduced within, a fictional work, universe, or narrative created by another entity.
-
E.
fictionalCharacterFrom
Indicates that a fictional character originates from, or is created within, a particular work, universe, or source.
- F. None of above. chosen
Provenance (4 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_69ca84d0123c819096f9dc3b6abb0881 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9e75abd48190a6e6679ec51496e8 |
completed | April 1, 2026, 10:38 p.m. |
| PD | Predicate disambiguation | batch_69cd03c6ffc88190a5e9569e19122ad5 |
completed | April 1, 2026, 11:38 a.m. |
| PDg | Predicate description generation | batch_69cd07c5c978819084abc7267a5ced80 |
completed | April 1, 2026, 11:55 a.m. |
Created at: March 30, 2026, 8:20 p.m.