Triple
T28161934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luhya |
E714917
|
entity |
| Predicate | citizenshipCommon |
P164085
|
FINISHED |
| Object | Kenyan |
—
|
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: Kenyan | Statement: [Luhya, citizenshipCommon, Kenyan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: citizenshipCommon Context triple: [Luhya, citizenshipCommon, Kenyan]
-
A.
citizenshipContext
Indicates the legal or social circumstances under which an entity holds or is granted citizenship in a particular state or jurisdiction.
-
B.
leaderCitizenship
Indicates that a leader holds citizenship in, or is a national of, a particular country or political entity.
-
C.
citizenshipIssue
Indicates that there is a problem, dispute, or complication concerning an entity’s citizenship status or rights.
-
D.
definedCitizenship
Indicates that a formal citizenship status has been legally established or specified for an entity.
-
E.
citizenshipType
Indicates the specific legal category or status of an individual's citizenship in relation to a state or country.
- F. None of above. chosen
Provenance (4 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_69efd6b156448190bfa15958208395c3 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69f641eb9eec8190a50b58f0f28983b8 |
completed | May 2, 2026, 6:26 p.m. |
| PD | Predicate disambiguation | batch_69f63c6c1a948190b68c0f92c264cc0c |
completed | May 2, 2026, 6:03 p.m. |
| PDg | Predicate description generation | batch_69f63fd4f7448190930c723ba7cfce62 |
completed | May 2, 2026, 6:17 p.m. |
Created at: April 27, 2026, 10:07 p.m.