Triple
T7640348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | মাইকেল মধুসূদন দত্ত |
E172983
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | প্যারিস |
E568
|
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: প্যারিস | Statement: [মাইকেল মধুসূদন দত্ত, residence, প্যারিস]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: প্যারিস Context triple: [মাইকেল মধুসূদন দত্ত, residence, প্যারিস]
-
A.
Parigi
Parigi is a coastal town that serves as the administrative center of Parigi Moutong Regency in Central Sulawesi, Indonesia.
-
B.
Parisi
Parisi is an Italian surname most notably associated with Giorgio Parisi, a Nobel Prize–winning theoretical physicist known for his work on complex systems and statistical mechanics.
-
C.
Parisii
The Parisii were a Celtic tribe of the Iron Age and Roman period who lived in the area of present-day Paris along the Seine River.
-
D.
Paris
chosen
Paris is the capital and largest city of France, renowned for its historic architecture, art, fashion, and cultural influence worldwide.
-
E.
Paris
Paris is a budget-oriented AMD Sempron processor core designed for entry-level desktop computing.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c6995360188190968ee57b72a1627f |
completed | March 27, 2026, 2:50 p.m. |
| NER | Named-entity recognition | batch_69c6facd9dec8190ab3af9cdde9992e6 |
completed | March 27, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8c7b5f1a48190b409230029c96fa8 |
completed | March 29, 2026, 6:33 a.m. |
Created at: March 27, 2026, 3:57 p.m.