Triple
T5943986
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meerut Cantt railway station |
E132234
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Muzaffarnagar |
E562849
|
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: Muzaffarnagar | Statement: [Meerut Cantt railway station, connectsTo, Muzaffarnagar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Muzaffarnagar Context triple: [Meerut Cantt railway station, connectsTo, Muzaffarnagar]
-
A.
Muzaffarnagar
chosen
Muzaffarnagar is a city in the Indian state of Uttar Pradesh, known as an agricultural and industrial center in the fertile Ganges-Yamuna Doab region.
-
B.
Ambala
Ambala is a historic city and important military and transportation hub in the northern Indian state of Haryana.
-
C.
Bulandshahr
Bulandshahr is a city in the Indian state of Uttar Pradesh known for its historical significance and proximity to Delhi within the broader metropolitan region.
-
D.
Moradabad
Moradabad is a major city in northern India known for its brass handicraft industry and is located in the state of Uttar Pradesh.
-
E.
Saharanpur
Saharanpur is a city in the Indian state of Uttar Pradesh known as a commercial and transportation hub, particularly for its wood carving industry and agricultural trade.
- 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_69c00869d3308190af89b2453e0f7546 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c03937b4a88190819a1fd63fc3d3ed |
completed | March 22, 2026, 6:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1355102c88190b139ba052e12a875 |
completed | March 23, 2026, 12:42 p.m. |
Created at: March 22, 2026, 4:01 p.m.