Triple
T8389918
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike Flanagan |
E197914
|
entity |
| Predicate | collaboratesWith |
P37
|
FINISHED |
| Object |
Rahul Kohli
Rahul Kohli is a British actor known for his genre work in television and film, particularly in horror and supernatural projects.
|
E732373
|
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: Rahul Kohli | Statement: [Mike Flanagan, collaboratesWith, Rahul Kohli]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rahul Kohli Context triple: [Mike Flanagan, collaboratesWith, Rahul Kohli]
-
A.
Sachit Mehra
Sachit Mehra is a Canadian political figure who serves in a top leadership role within the Liberal Party of Canada.
-
B.
Gautam Kumar
Gautam Kumar is known as the son of legendary Indian Bengali actor Uttam Kumar.
-
C.
Rahul Banga
Rahul Banga is an individual notable enough to be recognized as a prominent bearer of the surname Banga.
-
D.
Shantanu Thakur
Shantanu Thakur is an Indian politician from the Bharatiya Janata Party who serves as a Minister of State in the central government and represents the Bangaon constituency in the Lok Sabha.
-
E.
Abhishek Verma
Abhishek Verma is a computer scientist best known as a co-creator of Google Borg, the large-scale cluster management and scheduling system that inspired Kubernetes.
- 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: Rahul Kohli Triple: [Mike Flanagan, collaboratesWith, Rahul Kohli]
Generated description
Rahul Kohli is a British actor known for his genre work in television and film, particularly in horror and supernatural projects.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rahul Kohli Target entity description: Rahul Kohli is a British actor known for his genre work in television and film, particularly in horror and supernatural projects.
-
A.
Sachit Mehra
Sachit Mehra is a Canadian political figure who serves in a top leadership role within the Liberal Party of Canada.
-
B.
Gautam Kumar
Gautam Kumar is known as the son of legendary Indian Bengali actor Uttam Kumar.
-
C.
Rahul Banga
Rahul Banga is an individual notable enough to be recognized as a prominent bearer of the surname Banga.
-
D.
Shantanu Thakur
Shantanu Thakur is an Indian politician from the Bharatiya Janata Party who serves as a Minister of State in the central government and represents the Bangaon constituency in the Lok Sabha.
-
E.
Abhishek Verma
Abhishek Verma is a computer scientist best known as a co-creator of Google Borg, the large-scale cluster management and scheduling system that inspired Kubernetes.
- 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_69ca82f749388190bffbea6dfb509016 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb810ac380819095bd67f0555ac2a8 |
completed | March 31, 2026, 8:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce02cb3a1481908d30993d47c70039 |
completed | April 2, 2026, 5:46 a.m. |
| NEDg | Description generation | batch_69ce077f25648190b9a95fb72f5b4f8c |
completed | April 2, 2026, 6:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce08e192088190ad8170b1bedd568d |
completed | April 2, 2026, 6:12 a.m. |
Created at: March 30, 2026, 6:03 p.m.