Triple
T15909044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cady Heron |
E385797
|
entity |
| Predicate | raisedIn |
P7868
|
FINISHED |
| Object | Kenya |
E11292
|
NE 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: Kenya | Statement: [Cady Heron, raisedIn, Kenya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kenya Context triple: [Cady Heron, raisedIn, Kenya]
-
A.
Kenya
chosen
Kenya is an East African country known for its diverse wildlife, scenic landscapes from savannas to highlands, and a coastline along the Indian Ocean.
-
B.
Nyeri, Kenya
Nyeri, Kenya is a town in central Kenya near the Aberdare Range, known for its colonial-era history, agricultural economy, and as the final resting place of Scouting founder Robert Baden-Powell.
-
C.
Kamba (Kenya)
Kamba (Kenya) is a Bantu language spoken primarily by the Kamba people in eastern Kenya.
-
D.
Uganda
Uganda is a landlocked country in East Africa known for its diverse landscapes, abundant wildlife, and location along the equator.
-
E.
Kenie
Kenie is an alternative spelling of the given name Kenny, typically used as a personal name.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d86da686e4819097cbf3b1fc2d881d |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1565d2f048190a40379ceae00411a |
completed | April 16, 2026, 9:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb04d4d1c819091d9b3357ca0deca |
completed | May 9, 2026, 10:08 p.m. |
Created at: April 10, 2026, 4:52 a.m.