Triple
T11098537
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Felicitas |
E262440
|
entity |
| Predicate | typicalGenderOfBearer |
P34342
|
FINISHED |
| Object | female |
—
|
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: female | Statement: [Felicitas, typicalGenderOfBearer, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalGenderOfBearer Context triple: [Felicitas, typicalGenderOfBearer, female]
-
A.
genderOfTypicalHolder
chosen
Indicates the gender that is most commonly associated with or typical of the usual holder of something.
-
B.
hasTypicalGenderAssociation
Indicates that one entity is commonly or culturally associated with a particular gender more than with other genders.
-
C.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
-
D.
playsGender
Indicates that one entity performs or assumes a particular gender role or identity in a given context.
-
E.
genderImplication
Indicates that one entity’s gender suggests, constrains, or determines the possible or likely gender of another entity.
- 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_69d6aa9a40d88190a373e2c7e48285db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79a0b2890819081c4efc50e995cdd |
completed | April 9, 2026, 12:22 p.m. |
| PD | Predicate disambiguation | batch_69d7441aa3548190b92dbde57841c135 |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:27 p.m.