Triple
T10586601
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toposa language |
E249869
|
entity |
| Predicate | ethnologueEntry |
P19233
|
FINISHED |
| Object |
Toposa
Toposa is a Nilotic ethnic group primarily inhabiting southeastern South Sudan, known for their pastoralist lifestyle and rich oral traditions.
|
E872575
|
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: Toposa | Statement: [Toposa language, ethnologueEntry, Toposa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toposa Context triple: [Toposa language, ethnologueEntry, Toposa]
-
A.
Tuspa
Tuspa is an alternative name for Tushpa, the ancient capital city of the Urartian kingdom located near modern-day Lake Van in eastern Turkey.
-
B.
Tafoya
Tafoya is the surname of Michele Tafoya, a prominent American sportscaster best known for her work as an NFL sideline reporter.
-
C.
Orohena
Orohena is the highest peak on the island of Tahiti in French Polynesia, known for its rugged volcanic terrain and prominence in the Society Islands.
-
D.
Tezonco
Tezonco is a metro station in Mexico City that serves the southeastern area of the city on the capital’s rapid transit network.
-
E.
Mazunte
Mazunte is a small, laid-back beach town on Mexico’s Oaxacan coast, known for its sea turtle conservation center, eco-tourism, and scenic Pacific shoreline.
- 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: Toposa Triple: [Toposa language, ethnologueEntry, Toposa]
Generated description
Toposa is a Nilotic ethnic group primarily inhabiting southeastern South Sudan, known for their pastoralist lifestyle and rich oral traditions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Toposa Target entity description: Toposa is a Nilotic ethnic group primarily inhabiting southeastern South Sudan, known for their pastoralist lifestyle and rich oral traditions.
-
A.
Tuspa
Tuspa is an alternative name for Tushpa, the ancient capital city of the Urartian kingdom located near modern-day Lake Van in eastern Turkey.
-
B.
Tafoya
Tafoya is the surname of Michele Tafoya, a prominent American sportscaster best known for her work as an NFL sideline reporter.
-
C.
Orohena
Orohena is the highest peak on the island of Tahiti in French Polynesia, known for its rugged volcanic terrain and prominence in the Society Islands.
-
D.
Tezonco
Tezonco is a metro station in Mexico City that serves the southeastern area of the city on the capital’s rapid transit network.
-
E.
Mazunte
Mazunte is a small, laid-back beach town on Mexico’s Oaxacan coast, known for its sea turtle conservation center, eco-tourism, and scenic Pacific shoreline.
- 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_69d381c9d3d48190a29ee491e1696a0e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5276b0ae48190b2935230363239e0 |
completed | April 7, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d94b8b1b708190865e428128f98720 |
completed | April 10, 2026, 7:12 p.m. |
| NEDg | Description generation | batch_69d94d68f39c8190bc7ea90237a5bf5f |
completed | April 10, 2026, 7:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d9522d68b88190a63acb6d657168b4 |
completed | April 10, 2026, 7:40 p.m. |
Created at: April 6, 2026, 12:39 p.m.