Triple
T7663580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francis Crick Medal and Lecture |
E173568
|
entity |
| Predicate | notableRecipient |
P108
|
FINISHED |
| Object |
Madan Babu
Madan Babu is a computational biologist known for his influential work on gene regulation, protein networks, and systems biology.
|
E687339
|
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: Madan Babu | Statement: [Francis Crick Medal and Lecture, notableRecipient, Madan Babu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Madan Babu Context triple: [Francis Crick Medal and Lecture, notableRecipient, Madan Babu]
-
A.
Sanjay Sen
Sanjay Sen is known primarily as the husband of acclaimed Indian filmmaker and actress Aparna Sen.
-
B.
Paresh Babu
Paresh Babu is a central fictional character in Rabindranath Tagore’s Bengali novel "Gora," representing complex social and philosophical themes in colonial India.
-
C.
Uttam Kumar
Uttam Kumar was a legendary Indian actor and cultural icon, widely regarded as the greatest star of Bengali cinema.
-
D.
Amar Nath Chatterjee
Amar Nath Chatterjee is an Indian politician who has served as the mayor of Asansol in West Bengal.
-
E.
Sanjeev Kumar
Sanjeev Kumar was a highly acclaimed Indian film actor known for his versatile performances in both mainstream and parallel cinema during the 1960s and 1970s.
- 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: Madan Babu Triple: [Francis Crick Medal and Lecture, notableRecipient, Madan Babu]
Generated description
Madan Babu is a computational biologist known for his influential work on gene regulation, protein networks, and systems biology.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Madan Babu Target entity description: Madan Babu is a computational biologist known for his influential work on gene regulation, protein networks, and systems biology.
-
A.
Sanjay Sen
Sanjay Sen is known primarily as the husband of acclaimed Indian filmmaker and actress Aparna Sen.
-
B.
Paresh Babu
Paresh Babu is a central fictional character in Rabindranath Tagore’s Bengali novel "Gora," representing complex social and philosophical themes in colonial India.
-
C.
Uttam Kumar
Uttam Kumar was a legendary Indian actor and cultural icon, widely regarded as the greatest star of Bengali cinema.
-
D.
Amar Nath Chatterjee
Amar Nath Chatterjee is an Indian politician who has served as the mayor of Asansol in West Bengal.
-
E.
Sanjeev Kumar
Sanjeev Kumar was a highly acclaimed Indian film actor known for his versatile performances in both mainstream and parallel cinema during the 1960s and 1970s.
- 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_69c69955517c819085bc715b96d304d2 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c701a868bc8190b975cae769e23546 |
completed | March 27, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8c7b5f1a48190b409230029c96fa8 |
completed | March 29, 2026, 6:33 a.m. |
| NEDg | Description generation | batch_69c8c823ad70819096fa675d7478ddcc |
completed | March 29, 2026, 6:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8c87cad988190b5945b0f8de3b1ef |
completed | March 29, 2026, 6:36 a.m. |
Created at: March 27, 2026, 3:59 p.m.