Triple
T9921787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lalgarh Palace |
E187812
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Bikaner |
E35816
|
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: Bikaner | Statement: [Lalgarh Palace, city, Bikaner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bikaner Context triple: [Lalgarh Palace, city, Bikaner]
-
A.
Bikaner
chosen
Bikaner is a historic city in the Indian state of Rajasthan, known for its desert landscape, grand forts, and rich Rajasthani culture.
-
B.
Jodhpur
Jodhpur is a historic city in the Indian state of Rajasthan, renowned for its blue-painted old town, imposing Mehrangarh Fort, and role as a major cultural and commercial center on the edge of the Thar Desert.
-
C.
Jhunjhunu
Jhunjhunu is a city and district in the northeastern part of Rajasthan, India, known for its historic havelis, rich Marwari heritage, and role as a prominent center of education and military recruitment.
-
D.
Marwar
Marwar is a historic desert region in the western part of Rajasthan, India, known for its Rajput heritage, forts, and distinctive Marwari culture and language.
-
E.
Bhilwara
Bhilwara is a prominent industrial city in the Indian state of Rajasthan, known especially for its large textile and garment manufacturing sector.
- 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_69ca82b22a688190b52c75bd48429c10 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb56abbb88190a21b8b77f1a25b81 |
completed | April 2, 2026, 12:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d269d24ac4819081683e6ac5db7015 |
completed | April 5, 2026, 1:55 p.m. |
Created at: March 30, 2026, 8:42 p.m.