Triple
T8900194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autobiography of Francis Place |
E211910
|
entity |
| Predicate | hasBiographicalSubjectGender |
P9920
|
FINISHED |
| Object | male |
—
|
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: male | Statement: [Autobiography of Francis Place, hasBiographicalSubjectGender, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBiographicalSubjectGender Context triple: [Autobiography of Francis Place, hasBiographicalSubjectGender, male]
-
A.
genderOfEponym
Indicates the gender of the person after whom something (such as a place, object, or concept) is named.
-
B.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
C.
hasAuthorGender
chosen
Indicates that an entity (such as a work or publication) is associated with an author of a specified gender.
-
D.
hasBiographicalTheme
Indicates that something (such as a work, text, or content) centers on or significantly involves biographical subject matter, such as a person’s life, experiences, or personal history.
-
E.
hasLeadCharacterGender
Indicates that the primary or lead character in a work has a specified gender.
- 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_69ca83918d3081909b326fa3750cb8c8 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc64278b208190afc3dec64ecdb0f5 |
completed | April 1, 2026, 12:17 a.m. |
| PD | Predicate disambiguation | batch_69cc5c2bfb38819083d5eb1af8ccf4d6 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:54 p.m.