Triple
T22643537
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amar, Verbo Intransitivo |
E558892
|
entity |
| Predicate | hasFemaleLeadCharacter |
P21355
|
FINISHED |
| Object | Fräulein |
—
|
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: Fräulein | Statement: [Amar, Verbo Intransitivo, hasFemaleLeadCharacter, Fräulein]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFemaleLeadCharacter Context triple: [Amar, Verbo Intransitivo, hasFemaleLeadCharacter, Fräulein]
-
A.
hasFemaleCharacter
Indicates that an entity includes or features at least one female character.
-
B.
hasLeadCharacterGender
chosen
Indicates that the primary or lead character in a work has a specified gender.
-
C.
relationshipTypeWithFemaleLead
Indicates the type or nature of a relationship that an entity has with a female lead.
-
D.
hasStrongFemaleCharacters
Indicates that the work features prominent, well-developed female characters who display agency, complexity, and significant influence on the narrative or outcome.
-
E.
numberOfMainFemaleLeadsInWork
Indicates the number of primary female lead characters that appear in a given work.
- 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_69e24547f7fc819086e2c4ba3b979657 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1703577948190ae044df4f8500bfe |
completed | April 29, 2026, 2:43 a.m. |
| PD | Predicate disambiguation | batch_69ee6294c4c08190b7e4829f4b9af24b |
completed | April 26, 2026, 7:08 p.m. |
Created at: April 17, 2026, 3:05 p.m.