Triple
T1987735
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | West Chadic |
E43179
|
entity |
| Predicate | hasMajorLanguage |
P207
|
FINISHED |
| Object |
Sayanci
Sayanci is a West Chadic language spoken in parts of northern Nigeria.
|
E222785
|
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: Sayanci | Statement: [West Chadic, hasMajorLanguage, Sayanci]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sayanci Context triple: [West Chadic, hasMajorLanguage, Sayanci]
-
A.
Mr. Science
Mr. Science is a symbolic figure representing the ideals of modern scientific rationality and progress that Chinese intellectuals championed during the May Fourth Movement.
-
B.
In the Name of Science
In the Name of Science is the original title of Martin Gardner’s influential 1950 book critically examining pseudoscience and popular scientific misconceptions.
-
C.
Sagan
Sagan is a town in present-day Żagań, Poland, historically known as a center where the astronomer Johannes Kepler conducted part of his scientific work.
-
D.
Science Inc.
Science Inc. is a consumer products company known for developing and marketing innovative, data-driven brands such as the meal replacement drink Soylent.
-
E.
Technium
Technium is an exhibition floor at Amsterdam's NEMO Science Museum that showcases interactive science and technology displays for visitors.
- 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: Sayanci Triple: [West Chadic, hasMajorLanguage, Sayanci]
Generated description
Sayanci is a West Chadic language spoken in parts of northern Nigeria.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sayanci Target entity description: Sayanci is a West Chadic language spoken in parts of northern Nigeria.
-
A.
Mr. Science
Mr. Science is a symbolic figure representing the ideals of modern scientific rationality and progress that Chinese intellectuals championed during the May Fourth Movement.
-
B.
In the Name of Science
In the Name of Science is the original title of Martin Gardner’s influential 1950 book critically examining pseudoscience and popular scientific misconceptions.
-
C.
Sagan
Sagan is a town in present-day Żagań, Poland, historically known as a center where the astronomer Johannes Kepler conducted part of his scientific work.
-
D.
Science Inc.
Science Inc. is a consumer products company known for developing and marketing innovative, data-driven brands such as the meal replacement drink Soylent.
-
E.
Technium
Technium is an exhibition floor at Amsterdam's NEMO Science Museum that showcases interactive science and technology displays for visitors.
- 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_69a88713ddc88190a969715658ebe7a8 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb840a5708190a9b64564b855fb22 |
completed | March 7, 2026, 5:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae033410a88190bac79032a012549a |
completed | March 8, 2026, 11:16 p.m. |
| NEDg | Description generation | batch_69ae03e6239881909144a41a7ef96941 |
completed | March 8, 2026, 11:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae0452e188819099de641a9afcd04c |
completed | March 8, 2026, 11:20 p.m. |
Created at: March 4, 2026, 7:37 p.m.