Triple
T25108861
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. R. Ahmed Dental College and Hospital |
E628937
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Rafiuddin Ahmed
Rafiuddin Ahmed was a pioneering Indian dentist and educator, widely regarded as the father of modern dentistry in India.
|
E1668443
|
NE FINISHED |
How this triple was built (2 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: Rafiuddin Ahmed | Statement: [Dr. R. Ahmed Dental College and Hospital, namedAfter, Rafiuddin Ahmed]
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: Rafiuddin Ahmed Triple: [Dr. R. Ahmed Dental College and Hospital, namedAfter, Rafiuddin Ahmed]
Generated description
Rafiuddin Ahmed was a pioneering Indian dentist and educator, widely regarded as the father of modern dentistry in India.
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_69e2ff3169d08190973b6061d5009abd |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f4657448e88190993f7c497f8e808f |
completed | May 1, 2026, 8:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a105cf39d888190aeac2993bb278393 |
completed | May 22, 2026, 1:41 p.m. |
| NEDg | Description generation | batch_6a105e161df88190ba6a36e7581cd4ae |
completed | May 22, 2026, 1:45 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a105fa381408190b9343fb060d29374 |
completed | May 22, 2026, 1:52 p.m. |
Created at: April 18, 2026, 6:26 a.m.