Triple
T237071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sind |
E4844
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Rajasthan |
E9756
|
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: Rajasthan | Statement: [Sind, borders, Rajasthan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rajasthan Context triple: [Sind, borders, Rajasthan]
-
A.
Rajasthan
chosen
Rajasthan is a northwestern Indian state known for its vast Thar Desert, historic Rajput forts and palaces, and rich cultural heritage.
-
B.
Gujarat
Gujarat is a western coastal state of India known for its significant role in trade and industry, rich cultural heritage, and historic cities such as Ahmedabad.
-
C.
Chhattisgarh
Chhattisgarh is a state in central India known for its rich mineral resources, dense forests, tribal cultures, and growing industrial and power sectors.
-
D.
Madhya Pradesh
Madhya Pradesh is a large central Indian state known for its historical cities, diverse tribal cultures, and significant forested and wildlife areas including several major national parks.
-
E.
Uttar Pradesh
Uttar Pradesh is a populous northern Indian state known for its political influence, rich cultural and religious heritage, and historic cities such as Varanasi and Agra.
- 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25ccc5d548190b505bf1d99db41bd |
completed | Feb. 28, 2026, 3:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3d4dcaec0819099f5a3721d035cdd |
completed | March 1, 2026, 5:55 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.