Triple
T3951603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chadic languages |
E84876
|
entity |
| Predicate | majorLanguage |
P207
|
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: [Chadic languages, majorLanguage, Tera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tera Context triple: [Chadic languages, majorLanguage, 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.
Terra
Terra is a sustainability-themed character created as one of the official mascots for Expo 2020 Dubai, symbolizing environmental awareness and ecological responsibility.
-
D.
Maa
Maa is a Nilotic language spoken primarily by the Maasai people of Kenya and Tanzania.
-
E.
Urana
Urana is a small rural town in the Riverina region of New South Wales, Australia, known for its agricultural surroundings and historic country character.
- 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_69aed934fbfc8190847068e4546de963 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef9384cdc8190ad89b32dc25ac1d7 |
completed | March 9, 2026, 4:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b533a80d4c8190bb1aac1b2900d9a8 |
completed | March 14, 2026, 10:08 a.m. |
Created at: March 9, 2026, 3:30 p.m.