Triple
T23493403
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nivaclé |
E571635
|
entity |
| Predicate | relatedEthnicGroups |
P1969
|
FINISHED |
| Object |
Maká
The Maká are an Indigenous people of the Gran Chaco region in Paraguay and Argentina, known for their distinct language, traditional hunting and gathering practices, and rich ceremonial culture.
|
E1591340
|
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: Maká | Statement: [Nivaclé, relatedEthnicGroups, Maká]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maká Context triple: [Nivaclé, relatedEthnicGroups, Maká]
-
A.
Maklak
Maklak is the self-designated name (autonym) used by the Modoc people to refer to themselves.
-
B.
Makato
Makato is a coastal municipality in the province of Aklan in the Western Visayas region of the Philippines.
-
C.
Makuna
Makuna is an indigenous Tucanoan language spoken by the Makuna people of the northwest Amazon region, primarily in Colombia and Brazil.
-
D.
Makkena
Makkena is the surname of American actress Wendy Makkena, known for her roles in films such as "Sister Act" and various television series.
-
E.
Makum
Makum is a notable town in Assam, India, recognized historically as a coal-mining and railway hub within the Tinsukia district.
- 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: Maká Triple: [Nivaclé, relatedEthnicGroups, Maká]
Generated description
The Maká are an Indigenous people of the Gran Chaco region in Paraguay and Argentina, known for their distinct language, traditional hunting and gathering practices, and rich ceremonial culture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Maká Target entity description: The Maká are an Indigenous people of the Gran Chaco region in Paraguay and Argentina, known for their distinct language, traditional hunting and gathering practices, and rich ceremonial culture.
-
A.
Maklak
Maklak is the self-designated name (autonym) used by the Modoc people to refer to themselves.
-
B.
Makato
Makato is a coastal municipality in the province of Aklan in the Western Visayas region of the Philippines.
-
C.
Makuna
Makuna is an indigenous Tucanoan language spoken by the Makuna people of the northwest Amazon region, primarily in Colombia and Brazil.
-
D.
Makkena
Makkena is the surname of American actress Wendy Makkena, known for her roles in films such as "Sister Act" and various television series.
-
E.
Makum
Makum is a notable town in Assam, India, recognized historically as a coal-mining and railway hub within the Tinsukia district.
- 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_69e245b4829881909b77a70e942bbd54 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a7de1ab88190b6c2441c63a99713 |
completed | April 29, 2026, 6:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0cdf434de08190ae283b260c5d7579 |
completed | May 19, 2026, 10:08 p.m. |
| NEDg | Description generation | batch_6a0ce324b91481908741290de0f1e3ea |
completed | May 19, 2026, 10:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ce3e41c60819095760a2c5a224fb3 |
completed | May 19, 2026, 10:27 p.m. |
Created at: April 17, 2026, 6:05 p.m.