Triple
T6980907
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tamanic languages |
E161840
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object | Tamanic |
E632480
|
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: Tamanic | Statement: [Tamanic languages, hasAlternativeName, Tamanic]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tamanic Context triple: [Tamanic languages, hasAlternativeName, Tamanic]
-
A.
Tamanic
chosen
Tamanic is a small subgroup of Austronesian languages spoken primarily in parts of Borneo.
-
B.
Tallassee
Tallassee is a small city in central Alabama known for its location along the Tallapoosa River and its historic textile mill heritage.
-
C.
Tahlequah
Tahlequah is a city in eastern Oklahoma that serves as the capital of the Cherokee Nation and is known for its rich Native American history and culture.
-
D.
Timaná
Timaná is a municipality and town in southern Colombia known for its colonial heritage and agricultural economy within the Andean region.
-
E.
Mima
Mima is a character from the 1940 Bing Crosby and Bob Hope comedy film "Road to Singapore."
- 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_69c68855dc0481909b4c7e9e9ed273db |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db6c1efc8190ab1575ae2ce726db |
completed | March 27, 2026, 7:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c76a0e34288190ad2decbc18190c6b |
completed | March 28, 2026, 5:41 a.m. |
Created at: March 27, 2026, 2:31 p.m.