Triple
T12491519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phyno |
E298576
|
entity |
| Predicate | hasCollaboratedWith |
P8554
|
FINISHED |
| Object |
Vector
Vector is a prominent Nigerian rapper and songwriter known for his intricate wordplay, punchlines, and influential presence in the country’s hip-hop scene.
|
E988193
|
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: Vector | Statement: [Phyno, hasCollaboratedWith, Vector]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vector Context triple: [Phyno, hasCollaboratedWith, Vector]
-
A.
Vector
Vector is a mid-range, sport-oriented trim level of the Saab 9-3 that typically offers enhanced performance and upgraded interior and exterior features compared to base models.
-
B.
Vector
Vector is a villainous character from the Despicable Me franchise, known for his orange tracksuit, bowl haircut, and high-tech gadgets.
-
C.
Vector
Vector is a commercial vehicle brand produced by the Russian automotive manufacturer GAZ Group, known primarily for its buses.
-
D.
Vectors
"Vectors" is a science fiction work by American author Michael Kube-McDowell, known for its exploration of complex futuristic and technological themes.
-
E.
vec
vec is the ISO 639-3 code for the Venetian language, a Romance language spoken primarily in the Veneto region of Italy and surrounding areas.
- 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: Vector Triple: [Phyno, hasCollaboratedWith, Vector]
Generated description
Vector is a prominent Nigerian rapper and songwriter known for his intricate wordplay, punchlines, and influential presence in the country’s hip-hop scene.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vector Target entity description: Vector is a prominent Nigerian rapper and songwriter known for his intricate wordplay, punchlines, and influential presence in the country’s hip-hop scene.
-
A.
Vector
Vector is a mid-range, sport-oriented trim level of the Saab 9-3 that typically offers enhanced performance and upgraded interior and exterior features compared to base models.
-
B.
Vector
Vector is a villainous character from the Despicable Me franchise, known for his orange tracksuit, bowl haircut, and high-tech gadgets.
-
C.
Vector
Vector is a commercial vehicle brand produced by the Russian automotive manufacturer GAZ Group, known primarily for its buses.
-
D.
Vectors
"Vectors" is a science fiction work by American author Michael Kube-McDowell, known for its exploration of complex futuristic and technological themes.
-
E.
vec
vec is the ISO 639-3 code for the Venetian language, a Romance language spoken primarily in the Veneto region of Italy and surrounding areas.
- 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.