Triple
T8644108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maranhão Library in São Luís |
E204728
|
entity |
| Predicate | hasArchitectGender |
P9920
|
FINISHED |
| Object | female architect |
—
|
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 architect | Statement: [Maranhão Library in São Luís, hasArchitectGender, female architect]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasArchitectGender Context triple: [Maranhão Library in São Luís, hasArchitectGender, female architect]
-
A.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
B.
hasAuthorGender
chosen
Indicates that an entity (such as a work or publication) is associated with an author of a specified gender.
-
C.
hasGenderRole
Indicates that an entity is associated with, or expected to perform, a particular socially defined gender-based role or set of behaviors.
-
D.
hasPerformerGender
Indicates that an action, event, or performance is associated with the gender of the performer who carries it out.
-
E.
hasGenderVariant
Indicates that one entity is a gender-specific form or variant 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_69ca834ca1c88190a11ffb0200342fac |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc4798852881909c03c5eadf805e49 |
completed | March 31, 2026, 10:15 p.m. |
| PD | Predicate disambiguation | batch_69cc455d6d448190a2da2a319ac78c37 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:28 p.m.