Triple
T26893328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | মাদ্রাজ |
E677835
|
entity |
| Predicate | ঐতিহাসিক_নাম |
P28982
|
FINISHED |
| Object |
মাদ্রাজ প্রেসিডেন্সি রাজধানী
মাদ্রাজ প্রেসিডেন্সি রাজধানী ছিল ব্রিটিশ ভারতের মাদ্রাজ প্রেসিডেন্সির প্রশাসনিক ও রাজনৈতিক কেন্দ্র হিসেবে পরিচিত ঐতিহাসিক শহর।
|
E1748617
|
NE FINISHED |
How this triple was built (3 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: মাদ্রাজ প্রেসিডেন্সি রাজধানী | Statement: [মাদ্রাজ, ঐতিহাসিক_নাম, মাদ্রাজ প্রেসিডেন্সি রাজধানী]
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: মাদ্রাজ প্রেসিডেন্সি রাজধানী Triple: [মাদ্রাজ, ঐতিহাসিক_নাম, মাদ্রাজ প্রেসিডেন্সি রাজধানী]
Generated description
মাদ্রাজ প্রেসিডেন্সি রাজধানী ছিল ব্রিটিশ ভারতের মাদ্রাজ প্রেসিডেন্সির প্রশাসনিক ও রাজনৈতিক কেন্দ্র হিসেবে পরিচিত ঐতিহাসিক শহর।
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ঐতিহাসিক_নাম Context triple: [মাদ্রাজ, ঐতিহাসিক_নাম, মাদ্রাজ প্রেসিডেন্সি রাজধানী]
-
A.
historicalNameType
Indicates that the relationship specifies the type or category of a historical name associated with an entity.
-
B.
historicalNameGivenBy
Indicates that one entity is the name historically assigned to another entity by a specific source, culture, or period.
-
C.
regionHistoricalName
chosen
Indicates that a region has been known by a particular historical name during some past period.
-
D.
historicalNameUsedIn
Indicates that a historical or former name was used to refer to a particular entity in some context.
-
E.
historicalNameInEnglish
Indicates that an entity is associated with a historical English-language name by which it was known in the past.
- F. None of above.
Provenance (6 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_69eee9befee48190a26f214faa867be7 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f61f69e4508190ab20c3f2052282e7 |
completed | May 2, 2026, 3:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a121ea2fa008190913b10dedb3b67a6 |
completed | May 23, 2026, 9:39 p.m. |
| NEDg | Description generation | batch_6a121f90eec08190bd18be556349e464 |
completed | May 23, 2026, 9:43 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a1220621c4c81909da5a95967d52202 |
completed | May 23, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69f611af72ac819094598dd2530d7411 |
completed | May 2, 2026, 3:01 p.m. |
Created at: April 27, 2026, 5:46 a.m.