Triple
T29894972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Guest Book |
E759253
|
entity |
| Predicate | hasRecurringCharacters |
P201934
|
FINISHED |
| Object | local townspeople |
—
|
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: local townspeople | Statement: [The Guest Book, hasRecurringCharacters, local townspeople]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRecurringCharacters Context triple: [The Guest Book, hasRecurringCharacters, local townspeople]
-
A.
isRecurringCharacter
Indicates that an entity appears repeatedly or regularly within a given narrative, series, or context rather than only once.
-
B.
hasRecurringProtagonists
Indicates that the same main character or set of main characters appears repeatedly across multiple works or installments in a series.
-
C.
hasRecurringCharacterFrom
Indicates that one work or series includes a character who also appears recurrently in another work or series.
-
D.
hasRecurringSeriesProtagonists
Indicates that a recurring series features one or more protagonists who appear repeatedly across its installments.
-
E.
recurringCharacterInSeason
Indicates that a character appears repeatedly across multiple episodes within a specific season of a series.
- 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_69f2245f1cf88190978c70d1a1d2cb73 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_6a00372ff0e48190b3ed91f9bae9da6c |
completed | May 10, 2026, 7:43 a.m. |
| PD | Predicate disambiguation | batch_6a00359c1b8481909c1e43df9f5a789a |
completed | May 10, 2026, 7:37 a.m. |
| PDg | Predicate description generation | batch_6a00372f29548190bc5b8f0c0ece3cac |
completed | May 10, 2026, 7:43 a.m. |
Created at: April 29, 2026, 6:04 p.m.