Triple
T24857845
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ulrica |
E622074
|
entity |
| Predicate | hasFemaleTitleCharacter |
P157417
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Ulrica, hasFemaleTitleCharacter, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFemaleTitleCharacter Context triple: [Ulrica, hasFemaleTitleCharacter, true]
-
A.
hasFemaleCharacter
Indicates that an entity includes or features at least one female character.
-
B.
hasStrongFemaleCharacters
Indicates that the work features prominent, well-developed female characters who display agency, complexity, and significant influence on the narrative or outcome.
-
C.
hasLeadCharacterGender
Indicates that the primary or lead character in a work has a specified gender.
-
D.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
-
E.
hasGenderedTitle
Indicates that an entity is associated with a title or form of address that is explicitly marked for a particular gender.
- 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_69e2fac350d08190b3affde1b451a8c5 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f43043512481909501a3979cac9947 |
completed | May 1, 2026, 4:46 a.m. |
| PD | Predicate disambiguation | batch_69f420fd375c81908ea4a4e60b76ee8f |
completed | May 1, 2026, 3:41 a.m. |
| PDg | Predicate description generation | batch_69f4303fad6c8190844f069164f0904d |
completed | May 1, 2026, 4:46 a.m. |
Created at: April 18, 2026, 5:21 a.m.