Triple
T14208994
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gombe State |
E352175
|
entity |
| Predicate | majorEthnicGroup |
P1898
|
FINISHED |
| Object | Tera |
E222790
|
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: Tera | Statement: [Gombe State, majorEthnicGroup, Tera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tera Context triple: [Gombe State, majorEthnicGroup, Tera]
-
A.
Tera
chosen
Tera is a West Chadic language spoken primarily in northeastern Nigeria by the Tera people.
-
B.
Terah
Terah is a biblical patriarch known as the father of Abraham and a descendant of Shem who lived in Mesopotamia.
-
C.
Tierralta
Tierralta is a municipality and town in northern Colombia’s Córdoba Department, known for its rural setting and proximity to the Paramillo National Natural Park.
-
D.
Gaià
Gaià is a small rural municipality in the comarca of Bages in Catalonia, Spain.
-
E.
Terra
Terra is a character from the film "I, Frankenstein," depicted as a central figure within its dark, supernatural world of gargoyles and demons.
- 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_69d8278a06e481908b5d6af0a8afe737 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61fa8d24819092a8ec5d34c1c799 |
completed | April 14, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd19557f908190abb3dc116676f215 |
completed | May 7, 2026, 10:59 p.m. |
Created at: April 10, 2026, 1:05 a.m.