Triple
T7303037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karas Region |
E167904
|
entity |
| Predicate | hasCommonLanguage |
P741
|
FINISHED |
| Object | Nama |
E85862
|
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: Nama | Statement: [Karas Region, hasCommonLanguage, Nama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nama Context triple: [Karas Region, hasCommonLanguage, Nama]
-
A.
Nama
chosen
Nama is a Khoe language spoken primarily by the Nama people in Namibia and neighboring regions of southern Africa.
-
B.
Nome
Nome is a remote coastal city in western Alaska known historically for its gold rush heritage and as a key transportation and supply hub on the Bering Sea.
-
C.
Nom
Nom is a domain name marketplace and service platform operating under the brand Nom.com.
-
D.
Nume
Nume is an Oceanic language spoken on the island of Gaua in northern Vanuatu.
-
E.
Na
Na is the given name of Chinese professional tennis player Li Na, a former world No. 2 and two-time Grand Slam singles champion.
- 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_69c6888c820881909fc68f689fe1c251 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6ebb2261c8190ae9095c8e110b528 |
completed | March 27, 2026, 8:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7e558098c819091562566c59332e2 |
completed | March 28, 2026, 2:27 p.m. |
Created at: March 27, 2026, 3:01 p.m.