Triple
T2942360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Statue of Wendell Phillips (Boston) |
E79416
|
entity |
| Predicate | subjectSexOrGender |
P72
|
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: [Statue of Wendell Phillips (Boston), subjectSexOrGender, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectSexOrGender Context triple: [Statue of Wendell Phillips (Boston), subjectSexOrGender, male]
-
A.
sexOrGender
chosen
Indicates that one entity has a specified biological sex or socially constructed gender identity.
-
B.
sexType
Indicates the specific category or type of sexual activity or sexual relationship involved between entities.
-
C.
creatorSexOrGender
Indicates that the specified sex or gender is the sex or gender of the creator of the referenced work or entity.
-
D.
sexes
Indicates that one entity engages in sexual activity with another entity.
-
E.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
- 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_69ad8b1089588190b74d9e2505e45762 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9870e5d08190b3b277ba823fe6a1 |
completed | March 8, 2026, 3:40 p.m. |
| PD | Predicate disambiguation | batch_69ad96088fb481909976b436c2b729d9 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:56 p.m.