Triple
T2774139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr Eazi |
E61527
|
entity |
| Predicate | collaboratedWith |
P435
|
FINISHED |
| Object |
Tekno
Tekno is a Nigerian singer, songwriter, and record producer known for his Afrobeat and Afropop hit songs and dance-oriented sound.
|
E298317
|
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: Tekno | Statement: [Mr Eazi, collaboratedWith, Tekno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tekno Context triple: [Mr Eazi, collaboratedWith, Tekno]
-
A.
Tech
The Tech is a river in southern France that flows through the Pyrénées-Orientales in the Occitanie region before emptying into the Mediterranean Sea.
-
B.
Tek
Tek is a brand associated with Tektronix, known for electronic test and measurement equipment such as oscilloscopes and signal analyzers.
-
C.
TEC
TEC is the commonly used acronym for the Episcopal Church, a mainline Anglican Christian denomination based in the United States.
-
D.
Technium
Technium is an exhibition floor at Amsterdam's NEMO Science Museum that showcases interactive science and technology displays for visitors.
-
E.
Engadget
Engadget is a technology news and reviews website that covers consumer electronics, gadgets, and digital culture.
- 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: Tekno Triple: [Mr Eazi, collaboratedWith, Tekno]
Generated description
Tekno is a Nigerian singer, songwriter, and record producer known for his Afrobeat and Afropop hit songs and dance-oriented sound.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tekno Target entity description: Tekno is a Nigerian singer, songwriter, and record producer known for his Afrobeat and Afropop hit songs and dance-oriented sound.
-
A.
Tech
The Tech is a river in southern France that flows through the Pyrénées-Orientales in the Occitanie region before emptying into the Mediterranean Sea.
-
B.
Tek
Tek is a brand associated with Tektronix, known for electronic test and measurement equipment such as oscilloscopes and signal analyzers.
-
C.
TEC
TEC is the commonly used acronym for the Episcopal Church, a mainline Anglican Christian denomination based in the United States.
-
D.
TMT
TMT is a planned next-generation ground-based optical and infrared observatory featuring a 30-meter primary mirror for extremely high-resolution astronomical observations.
-
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_69ab4b7cd13481909174bca9809ed259 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdd7e29e08190921fd4ac9d0679ec |
completed | March 7, 2026, 8:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc0553588819098d478b4aaa18478 |
completed | March 10, 2026, 6:55 a.m. |
| NEDg | Description generation | batch_69afc1c74ecc8190ba8b9926ff28f24e |
completed | March 10, 2026, 7:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afc2d5aecc81909ce90a7e891568c9 |
completed | March 10, 2026, 7:05 a.m. |
Created at: March 6, 2026, 9:57 p.m.