Triple
T721233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fang language |
E14619
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object |
Nzaman
Nzaman is a dialect of the Fang language spoken by Fang communities in Central Africa.
|
E92734
|
NE FINISHED |
How this triple was built (4 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: Nzaman | Statement: [Fang language, hasDialect, Nzaman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nzaman Context triple: [Fang language, hasDialect, Nzaman]
-
A.
Mwanza
Mwanza is a major port city in northwestern Tanzania, situated on the southern shores of Lake Victoria and serving as a key commercial and transport hub for the region.
-
B.
Nini
Nini is one of the five Fuwa mascots of the 2008 Beijing Summer Olympics, inspired by a swallow and symbolizing good luck and the host city's culture.
-
C.
Wiphala
The Wiphala is a multicolored, checkered flag representing the Indigenous peoples of the Andes and widely recognized as a symbol of Indigenous identity and plurinationalism in Bolivia.
-
D.
Negaraku
Negaraku is the national anthem of Malaysia, symbolizing the country's sovereignty and unity.
-
E.
Namba
Namba is a major commercial and entertainment district in Osaka, Japan, known for its bustling nightlife, shopping, and iconic neon-lit streets.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Nzaman Triple: [Fang language, hasDialect, Nzaman]
Generated description
Nzaman is a dialect of the Fang language spoken by Fang communities in Central Africa.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nzaman Target entity description: Nzaman is a dialect of the Fang language spoken by Fang communities in Central Africa.
-
A.
Mwanza
Mwanza is a major port city in northwestern Tanzania, situated on the southern shores of Lake Victoria and serving as a key commercial and transport hub for the region.
-
B.
Nini
Nini is one of the five Fuwa mascots of the 2008 Beijing Summer Olympics, inspired by a swallow and symbolizing good luck and the host city's culture.
-
C.
Wiphala
The Wiphala is a multicolored, checkered flag representing the Indigenous peoples of the Andes and widely recognized as a symbol of Indigenous identity and plurinationalism in Bolivia.
-
D.
Negaraku
Negaraku is the national anthem of Malaysia, symbolizing the country's sovereignty and unity.
-
E.
Namba
Namba is a major commercial and entertainment district in Osaka, Japan, known for its bustling nightlife, shopping, and iconic neon-lit streets.
- F. None of above. chosen
Provenance (5 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_69a4934c753c81909b309027e48b9b3a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a58fa41c819082de2cc4e0cb2943 |
completed | March 1, 2026, 8:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a673329a0c8190b725b9863ba4c162 |
completed | March 3, 2026, 5:35 a.m. |
| NEDg | Description generation | batch_69a673afbd308190863aea2ff0f650cb |
completed | March 3, 2026, 5:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a6743f50d0819089885836b9668466 |
completed | March 3, 2026, 5:40 a.m. |
Created at: March 1, 2026, 7:37 p.m.