Triple
T18797948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eastern Mono |
E459685
|
entity |
| Predicate | subdivisionOf |
P258
|
FINISHED |
| Object | Mono language |
—
|
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: Mono language | Statement: [Eastern Mono, subdivisionOf, Mono language]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mono language Context triple: [Eastern Mono, subdivisionOf, Mono language]
-
A.
Mono language
chosen
Mono language is a Native American Uto-Aztecan language traditionally spoken by the Mono people of eastern California.
-
B.
Mono language
Mono is an Oceanic language spoken by the Mono people of the Treasury Islands in the Solomon Islands.
-
C.
Mon language
Mon language is an Austroasiatic language historically spoken in parts of Myanmar and Thailand, notable for its ancient literary tradition and influence on regional scripts and cultures.
-
D.
Mimi-D language
Mimi-D is an extinct and poorly documented language of Chad, historically spoken by a small ethnic group and only fragmentarily known from early 20th-century records.
-
E.
Lingo
Lingo is a scripting language primarily known for powering interactive multimedia applications and games in Adobe (formerly Macromedia) Director.
- 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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a020821881909749f6a1c6cd195b |
completed | April 20, 2026, 3:40 a.m. |
Created at: April 10, 2026, 11:53 a.m.