Triple
T23256834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Poro society |
E581896
|
entity |
| Predicate | counterpartGender |
P1613
|
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: [Poro society, counterpartGender, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: counterpartGender Context triple: [Poro society, counterpartGender, female]
-
A.
counterpartRelation
Indicates a reciprocal relationship where two entities serve as corresponding or equivalent counterparts to each other in a given context.
-
B.
counterpartEnglishName
Indicates that an entity has a corresponding counterpart whose name is given in English.
-
C.
hasCounterpart
Indicates that one entity corresponds to, matches, or serves as an equivalent or parallel version of another entity.
-
D.
hasFemaleEquivalent
chosen
Indicates that one entity serves as the female counterpart or equivalent of another entity.
-
E.
maleEquivalent
Indicates that one entity is the corresponding male counterpart or equivalent 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_69e246079f58819085eaa9c260906880 |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f194c4905c819099ca21c6529413ac |
completed | April 29, 2026, 5:19 a.m. |
| PD | Predicate disambiguation | batch_69effce4d704819092826931d430e8c4 |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:11 p.m.