Triple
T37358501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Juan by Tirso de Molina |
E927516
|
entity |
| Predicate | characterTypeIntroduced |
P198720
|
FINISHED |
| Object | Don Juan archetype |
—
|
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: Don Juan archetype | Statement: [Don Juan by Tirso de Molina, characterTypeIntroduced, Don Juan archetype]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterTypeIntroduced Context triple: [Don Juan by Tirso de Molina, characterTypeIntroduced, Don Juan archetype]
-
A.
introducedCharacterType
chosen
Indicates that one entity is responsible for introducing or first presenting a particular type or category of character.
-
B.
introducedByCharacter
Indicates that one character is responsible for presenting, bringing into the story, or otherwise causing another entity to be first revealed or made known.
-
C.
introducedByCharacters
Indicates that something (such as an idea, object, or event) is brought into the narrative or context by specific characters.
-
D.
characterCreatedInYear
Indicates the specific calendar year in which a fictional or narrative character was first created or introduced.
-
E.
featuresCharacterDebut
Indicates that the subject work includes the first-ever appearance of a particular character.
- F. None of above.
Provenance (3 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_69f76eb701788190b40824bc4594d985 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69ffe12ee59c8190bc7da386e6d5332d |
completed | May 10, 2026, 1:36 a.m. |
| PD | Predicate disambiguation | batch_69ffe0a138bc8190a3d4b48cd579e985 |
completed | May 10, 2026, 1:34 a.m. |
Created at: May 3, 2026, 4:16 p.m.