Triple
T14970871
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mai Mandir |
E373313
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Nadiad |
E373389
|
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: Nadiad | Statement: [Mai Mandir, city, Nadiad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nadiad Context triple: [Mai Mandir, city, Nadiad]
-
A.
Nadiad
chosen
Nadiad is a city in the Indian state of Gujarat, historically notable as the birthplace of independence leader Sardar Vallabhbhai Patel.
-
B.
Raisinghnagar
Raisinghnagar is a town and administrative center in the northern Indian state of Rajasthan, known for its agricultural economy within the arid region near the India–Pakistan border.
-
C.
Randhawa
Randhawa is an Indian-origin Punjabi surname notably borne by American politician Nikki Haley.
-
D.
Bathinda
Bathinda is a major city in southwestern Punjab, India, known as an important agricultural, industrial, and military center with historical forts and thermal power plants.
-
E.
Sardarshahar
Sardarshahar is a town in the Indian state of Rajasthan known for its historic havelis, temples, and traditional Rajasthani culture.
- 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_69d85ccbbcd48190acb56e7cf104d8ad |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6e59a7c8190a1634a706ea68fda |
completed | April 15, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe8be6ce68819099f841d83c6ca33d |
completed | May 9, 2026, 1:20 a.m. |
Created at: April 10, 2026, 2:49 a.m.