Triple
T12491511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phyno |
E298576
|
entity |
| Predicate | hasCollaboratedWith |
P8554
|
FINISHED |
| Object |
Teni
Teni is a Nigerian singer-songwriter and entertainer known for her catchy Afropop hits and playful, charismatic style.
|
E988188
|
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: Teni | Statement: [Phyno, hasCollaboratedWith, Teni]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teni Context triple: [Phyno, hasCollaboratedWith, Teni]
-
A.
Tinée
Tinée is a river in southeastern France that flows through the Alpes-Maritimes department in the Provence-Alpes-Côte d'Azur region.
-
B.
Ta’aisha
The Ta’aisha are a Sudanese Arab tribal group from the Darfur–Kordofan region, historically prominent through their leadership role in the Mahdist state under Abdallahi ibn Muhammad.
-
C.
Ten’edn
Ten’edn is an Aslian language spoken by an indigenous community in the Malay Peninsula, belonging to the broader Austroasiatic language family.
-
D.
Terêna
Terêna is an Arawakan language spoken by the Terena Indigenous people of Brazil, primarily in the state of Mato Grosso do Sul.
-
E.
Teniade Saraki
Teniade Saraki is a member of the prominent Saraki family of Nigerian politicians and public figures.
- 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: Teni Triple: [Phyno, hasCollaboratedWith, Teni]
Generated description
Teni is a Nigerian singer-songwriter and entertainer known for her catchy Afropop hits and playful, charismatic style.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Teni Target entity description: Teni is a Nigerian singer-songwriter and entertainer known for her catchy Afropop hits and playful, charismatic style.
-
A.
Tinée
Tinée is a river in southeastern France that flows through the Alpes-Maritimes department in the Provence-Alpes-Côte d'Azur region.
-
B.
Ta’aisha
The Ta’aisha are a Sudanese Arab tribal group from the Darfur–Kordofan region, historically prominent through their leadership role in the Mahdist state under Abdallahi ibn Muhammad.
-
C.
Ten’edn
Ten’edn is an Aslian language spoken by an indigenous community in the Malay Peninsula, belonging to the broader Austroasiatic language family.
-
D.
Terêna
Terêna is an Arawakan language spoken by the Terena Indigenous people of Brazil, primarily in the state of Mato Grosso do Sul.
-
E.
Teniade Saraki
Teniade Saraki is a member of the prominent Saraki family of Nigerian politicians and public figures.
- 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_69d6ada377208190a36011199a4d8558 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94de3076c81909640c982d520ca6b |
completed | April 10, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64ba9e1108190b74984d9da9baebe |
completed | May 2, 2026, 7:08 p.m. |
| NEDg | Description generation | batch_69f64c535c9881908e5bf07d13fa73c5 |
completed | May 2, 2026, 7:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6508afef08190ac7a19b1ee90141e |
completed | May 2, 2026, 7:29 p.m. |
Created at: April 8, 2026, 9:56 p.m.