Triple
T19166405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anyako, Ghana |
E469194
|
entity |
| Predicate | locatedInCountry |
P40
|
FINISHED |
| Object | Ghana |
—
|
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: Ghana | Statement: [Anyako, Ghana, locatedInCountry, Ghana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ghana Context triple: [Anyako, Ghana, locatedInCountry, Ghana]
-
A.
Ghana
chosen
Ghana is a West African nation known for being the first sub-Saharan African country to gain independence from colonial rule and for its stable democracy and rich cultural heritage.
-
B.
Anyako, Ghana
Anyako, Ghana is a small coastal town in the Volta Region known as the hometown of internationally acclaimed sculptor El Anatsui.
-
C.
Ghan
The Ghan is a famous Australian long-distance passenger train that runs through the continent’s interior between Adelaide and Darwin.
-
D.
La Guinea
La Guinea is a small settlement located on Isla del Rey in Spain’s Balearic Islands.
-
E.
Côte d'Ivoire
Côte d'Ivoire is a West African country on the Gulf of Guinea known for its cocoa production, diverse cultures, and economic prominence in the region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dd09d5a081909ae43c286651ae5a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f15ee064819087f9fd822236298f |
completed | April 20, 2026, 9:26 a.m. |
Created at: April 10, 2026, 12:06 p.m.