Triple
T31157181
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kwabena |
E794240
|
entity |
| Predicate | correspondingFemaleName |
P158000
|
FINISHED |
| Object | Abena |
—
|
NE NERFINISHED |
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: Abena | Statement: [Kwabena, correspondingFemaleName, Abena]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: correspondingFemaleName Context triple: [Kwabena, correspondingFemaleName, Abena]
-
A.
femaleCounterpartOf
chosen
Indicates that one entity is the female equivalent or corresponding counterpart of another entity within a given role, relationship, or category.
-
B.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
-
C.
namedForGender
Indicates that one entity is named in a way that reflects or is derived from a particular gender or gender-related characteristic of another entity.
-
D.
hasFemaleFormOf
Indicates that one entity is the specifically female version or form of another, more general or differently gendered entity.
-
E.
correspondsToSurname
Indicates that one entity is the surname or family name associated with, matching, or representing the other 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_69f224d504908190b01278dcb7fc3fa7 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f71996e1a48190ac59a1d66d7c44e8 |
completed | May 3, 2026, 9:47 a.m. |
| PD | Predicate disambiguation | batch_69f71820c6c88190ab38b4fa626d22cc |
completed | May 3, 2026, 9:40 a.m. |
Created at: April 29, 2026, 9:07 p.m.