Triple
T29799960
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucy Ashton |
E756663
|
entity |
| Predicate | influencedCharacterType |
P36788
|
FINISHED |
| Object | 19th-century operatic heroines |
—
|
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: 19th-century operatic heroines | Statement: [Lucy Ashton, influencedCharacterType, 19th-century operatic heroines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: influencedCharacterType Context triple: [Lucy Ashton, influencedCharacterType, 19th-century operatic heroines]
-
A.
influencesCharacter
chosen
Indicates that one entity affects, shapes, or alters the traits, behavior, or development of another entity’s character.
-
B.
influencedPerson
Indicates that one entity has affected, shaped, or guided the thoughts, behavior, or development of another person.
-
C.
wereInfluencedBy
Indicates that one entity’s ideas, actions, or characteristics were shaped or affected by another entity.
-
D.
influencedIn
Indicates that one entity had an effect on or shaped another entity within a specific context, domain, or setting.
-
E.
relatedCharacterType
Indicates that one character has a specified type of relationship or role in connection to another 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_69f22454583081908927516cb9938d1d |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f7465687bc8190a9da44d62b634ed7 |
completed | May 3, 2026, 12:57 p.m. |
| PD | Predicate disambiguation | batch_69f743f4ceb08190a21fe7f4a99b166b |
completed | May 3, 2026, 12:47 p.m. |
Created at: April 29, 2026, 5:17 p.m.