Triple
T5271919
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carmelite convent in the Faubourg Saint-Jacques, Paris |
E119278
|
entity |
| Predicate | genderOfResidents |
P62762
|
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: [Carmelite convent in the Faubourg Saint-Jacques, Paris, genderOfResidents, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderOfResidents Context triple: [Carmelite convent in the Faubourg Saint-Jacques, Paris, genderOfResidents, female]
-
A.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
B.
featuredGender
Indicates that a particular gender is highlighted, emphasized, or given primary focus in a given context or presentation.
-
C.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
-
D.
genderSignificance
Indicates the relevance or impact that an entity’s gender has within a particular context, relationship, or interpretation.
-
E.
genderDivision
Indicates a relationship where roles, responsibilities, or categories are separated or distinguished based on 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_69bd446c38e081908cdaf113bdf86790 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7d5a23908190a24e79d1b29d6fcf |
completed | March 20, 2026, 5:01 p.m. |
| PD | Predicate disambiguation | batch_69bd77c71268819094f9f5203eed392d |
completed | March 20, 2026, 4:37 p.m. |
| PDg | Predicate description generation | batch_69bd7d5906d88190b805977e5a05767a |
completed | March 20, 2026, 5:01 p.m. |
Created at: March 20, 2026, 1:51 p.m.