Triple
T12841674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Magha |
E307065
|
entity |
| Predicate | follows |
P134
|
FINISHED |
| Object |
Pausha
Pausha is a winter month in the traditional Hindu lunar calendar, typically corresponding to December–January in the Gregorian calendar.
|
E1003640
|
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: Pausha | Statement: [Magha, follows, Pausha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pausha Context triple: [Magha, follows, Pausha]
-
A.
Sawan
Sawan is a holy month in the Hindu calendar, devoted especially to the worship of Lord Shiva and marked by fasting, pilgrimages, and religious festivals.
-
B.
Margazhi
Margazhi is a winter month in the Tamil calendar, traditionally associated with religious observances, devotional music, and early-morning temple rituals.
-
C.
Ashadha
Ashadha is a month in the traditional Hindu lunar calendar, typically falling around June–July, associated with the onset of the monsoon season in the Indian subcontinent.
-
D.
Uttar Falguni
Uttar Falguni is a classic Bengali film best known for featuring an acclaimed dual-role performance by legendary actress Suchitra Sen.
-
E.
Phagun
Phagun is a late-winter month in the traditional Assamese calendar, roughly corresponding to February–March in the Gregorian calendar.
- 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: Pausha Triple: [Magha, follows, Pausha]
Generated description
Pausha is a winter month in the traditional Hindu lunar calendar, typically corresponding to December–January in the Gregorian calendar.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pausha Target entity description: Pausha is a winter month in the traditional Hindu lunar calendar, typically corresponding to December–January in the Gregorian calendar.
-
A.
Sawan
Sawan is a holy month in the Hindu calendar, devoted especially to the worship of Lord Shiva and marked by fasting, pilgrimages, and religious festivals.
-
B.
Margazhi
Margazhi is a winter month in the Tamil calendar, traditionally associated with religious observances, devotional music, and early-morning temple rituals.
-
C.
Ashadha
Ashadha is a month in the traditional Hindu lunar calendar, typically falling around June–July, associated with the onset of the monsoon season in the Indian subcontinent.
-
D.
Uttar Falguni
Uttar Falguni is a classic Bengali film best known for featuring an acclaimed dual-role performance by legendary actress Suchitra Sen.
-
E.
Phagun
Phagun is a late-winter month in the traditional Assamese calendar, roughly corresponding to February–March in the Gregorian calendar.
- 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_69d7bdf52b94819096d6f0ba4ab50a98 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96ff2ab60819085561a3120189985 |
completed | April 10, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f68edeaa1881909e06600ef88727de |
completed | May 2, 2026, 11:55 p.m. |
| NEDg | Description generation | batch_69f68f8e29508190b9c5b5ed88631bf8 |
completed | May 2, 2026, 11:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f69006c0288190a49ba8714cd19959 |
completed | May 3, 2026, midnight |
Created at: April 9, 2026, 5:35 p.m.