Triple
T9312857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RFC 7720 |
E224045
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object |
Warren Kumari
Warren Kumari is a network engineer and Internet standards contributor known for his work within the IETF and contributions to core Internet infrastructure specifications.
|
E791338
|
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: Warren Kumari | Statement: [RFC 7720, author, Warren Kumari]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Warren Kumari Context triple: [RFC 7720, author, Warren Kumari]
-
A.
Koli Patel
Koli Patel is a subgroup of the Kolis, a traditional coastal and agrarian community primarily found in western India.
-
B.
Asheem Chandna
Asheem Chandna is a prominent venture capitalist known for investing in and advising leading enterprise technology and cybersecurity startups.
-
C.
Loveleen Tandan
Loveleen Tandan is an Indian film director and casting director best known for her co-directing work on the Academy Award–winning film "Slumdog Millionaire."
-
D.
Shailen Mukherjee
Shailen Mukherjee was an Indian actor known for his role in Satyajit Ray’s acclaimed Bengali film "Charulata."
-
E.
Santosh Patel
Santosh Patel is the practical, zoo-owning father of protagonist Piscine Molitor Patel in Yann Martel’s novel "Life of Pi."
- 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: Warren Kumari Triple: [RFC 7720, author, Warren Kumari]
Generated description
Warren Kumari is a network engineer and Internet standards contributor known for his work within the IETF and contributions to core Internet infrastructure specifications.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Warren Kumari Target entity description: Warren Kumari is a network engineer and Internet standards contributor known for his work within the IETF and contributions to core Internet infrastructure specifications.
-
A.
Koli Patel
Koli Patel is a subgroup of the Kolis, a traditional coastal and agrarian community primarily found in western India.
-
B.
Asheem Chandna
Asheem Chandna is a prominent venture capitalist known for investing in and advising leading enterprise technology and cybersecurity startups.
-
C.
Loveleen Tandan
Loveleen Tandan is an Indian film director and casting director best known for her co-directing work on the Academy Award–winning film "Slumdog Millionaire."
-
D.
Shailen Mukherjee
Shailen Mukherjee was an Indian actor known for his role in Satyajit Ray’s acclaimed Bengali film "Charulata."
-
E.
Santosh Patel
Santosh Patel is the practical, zoo-owning father of protagonist Piscine Molitor Patel in Yann Martel’s novel "Life of Pi."
- 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_69ca8425f4fc81909c1c586e9a5b7530 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd20ae96e481909a1af9ea1c91f2b2 |
completed | April 1, 2026, 1:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0c7a0adc4819097ac906f03f0188e |
completed | April 4, 2026, 8:11 a.m. |
| NEDg | Description generation | batch_69d0c8a7190c819097e71c15f7924268 |
completed | April 4, 2026, 8:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d0c9e7e7d08190bddc6786f0fcea9e |
completed | April 4, 2026, 8:20 a.m. |
Created at: March 30, 2026, 7:37 p.m.