Triple
T12375624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Munger |
E295114
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object | Monghyr |
E190832
|
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: Monghyr | Statement: [Munger, formerName, Monghyr]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monghyr Context triple: [Munger, formerName, Monghyr]
-
A.
Monghyr
chosen
Monghyr (now usually spelled Munger) is a historic city in the Indian state of Bihar, known for its strategic location on the Ganges and its significance in various ancient and medieval Indian kingdoms.
-
B.
Khorlim
Khorlim is a village located in the North Goa district of the Indian state of Goa.
-
C.
Samalkha
Samalkha is a town in the northern Indian state of Haryana, known for its industrial activity and location along major transport routes.
-
D.
Chonyi
Chonyi is one of the Mijikenda sub-groups of the coastal Bantu communities in Kenya, known for its distinct language and cultural traditions.
-
E.
Monyash
Monyash is a small historic village in the Derbyshire Peak District of England, known for its limestone scenery and proximity to popular walking areas.
- 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_69d6ab6d8a4081908636601e69ddf262 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93fb8d6c081909e8bbbd52c73f29c |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f634760210819080bc0261aa059132 |
completed | May 2, 2026, 5:29 p.m. |
Created at: April 8, 2026, 9:54 p.m.