Triple
T2533997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Atlantic–Congo languages |
E56225
|
entity |
| Predicate | includesLanguage |
P2177
|
FINISHED |
| Object |
Sango
Sango is a Central African lingua franca and national language of the Central African Republic, originating as a Ngbandi-based trade language.
|
E274564
|
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: Sango | Statement: [Atlantic–Congo languages, includesLanguage, Sango]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sango Context triple: [Atlantic–Congo languages, includesLanguage, Sango]
-
A.
Shingu
Shingu is a coastal city in Japan known for its historic Kumano Hongu Taisha shrine and its role as a gateway to the sacred Kumano Kodo pilgrimage routes.
-
B.
Nakanamanga
Nakanamanga is an Oceanic Austronesian language spoken primarily on Efate Island and nearby areas in Vanuatu.
-
C.
Sonamura
Sonamura is a town in the Indian state of Tripura, known as an administrative and commercial center near the India–Bangladesh border.
-
D.
Oghi
Oghi is a town in Pakistan's Khyber Pakhtunkhwa province, known as a local administrative and commercial center within the Hazara region.
-
E.
Moruya
Moruya is a coastal town in New South Wales, Australia, known for its scenic river setting, nearby beaches, and historic granite quarries.
- 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: Sango Triple: [Atlantic–Congo languages, includesLanguage, Sango]
Generated description
Sango is a Central African lingua franca and national language of the Central African Republic, originating as a Ngbandi-based trade language.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sango Target entity description: Sango is a Central African lingua franca and national language of the Central African Republic, originating as a Ngbandi-based trade language.
-
A.
Shingu
Shingu is a coastal city in Japan known for its historic Kumano Hongu Taisha shrine and its role as a gateway to the sacred Kumano Kodo pilgrimage routes.
-
B.
Nakanamanga
Nakanamanga is an Oceanic Austronesian language spoken primarily on Efate Island and nearby areas in Vanuatu.
-
C.
Sonamura
Sonamura is a town in the Indian state of Tripura, known as an administrative and commercial center near the India–Bangladesh border.
-
D.
Oghi
Oghi is a town in Pakistan's Khyber Pakhtunkhwa province, known as a local administrative and commercial center within the Hazara region.
-
E.
Moruya
Moruya is a coastal town in New South Wales, Australia, known for its scenic river setting, nearby beaches, and historic granite quarries.
- 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_69ab4a49b6508190bc467fbef4bac334 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd27afe7c8190984e10d3f3d5586b |
completed | March 7, 2026, 7:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af2bbc416c81908774782420b54664 |
completed | March 9, 2026, 8:21 p.m. |
| NEDg | Description generation | batch_69af4c5e49dc8190920612a8b0f5b3f7 |
completed | March 9, 2026, 10:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69af4cd3bcc8819091589f0aa27ddc5d |
completed | March 9, 2026, 10:42 p.m. |
Created at: March 6, 2026, 9:47 p.m.