Triple
T10489834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Surmic languages |
E247385
|
entity |
| Predicate | includesLanguage |
P2177
|
FINISHED |
| Object | Murle language |
E252178
|
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: Murle language | Statement: [Surmic languages, includesLanguage, Murle language]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Murle language Context triple: [Surmic languages, includesLanguage, Murle language]
-
A.
Murle language
chosen
The Murle language is an Eastern Sudanic language spoken primarily by the Murle people of South Sudan.
-
B.
Malasanga language
The Malasanga language is an Oceanic language spoken in Papua New Guinea, belonging to the Kula–Malasanga subgroup of the Austronesian language family.
-
C.
Warji language
Warji language is a West Chadic language spoken primarily in Bauchi State, northern Nigeria, by the Warji people.
-
D.
Marakwet language
The Marakwet language is a Southern Nilotic language spoken by the Marakwet people of Kenya and is closely related to other Kalenjin languages such as Kipsigis.
-
E.
Sanglechi language
The Sanglechi language is an Eastern Iranian language spoken by a small community in the Sanglech Valley region of Afghanistan and Tajikistan.
- 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_69d381c309b88190af78aa681cf6a4c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5097ca5c081908b47a08ca7885650 |
completed | April 7, 2026, 1:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d94b0d05a88190be036a6e4ab374a7 |
completed | April 10, 2026, 7:10 p.m. |
Created at: April 6, 2026, 12:23 p.m.