Triple
T20204481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kheragarh |
E493312
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Agra |
—
|
NE NERFINISHED |
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: Agra | Statement: [Kheragarh, nearbyCity, Agra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Agra Context triple: [Kheragarh, nearbyCity, Agra]
-
A.
Agra
chosen
Agra is a historic city in northern India renowned for its Mughal-era architecture, most notably the Taj Mahal and Agra Fort.
-
B.
AGRA
AGRA is an African-based organization focused on transforming smallholder agriculture to improve food security and incomes across the continent.
-
C.
Agra Cantt
Agra Cantt is the main railway station and a major rail hub in Agra, Uttar Pradesh, serving as a key junction on several important long-distance routes across India.
-
D.
Agra metropolitan area
The Agra metropolitan area is the urban agglomeration centered on the historic city of Agra in Uttar Pradesh, India, encompassing its surrounding suburbs and satellite localities.
-
E.
Ayodhya
Ayodhya is an ancient city in India revered in Hinduism as the birthplace of Lord Rama and a major pilgrimage site.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da6269614c8190bb40475d9d477358 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66d8f90108190b72e37c0056de0f8 |
completed | April 20, 2026, 6:16 p.m. |
Created at: April 11, 2026, 11:38 p.m.