Triple
T961724
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central India |
E20748
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Guna |
E108085
|
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: Guna | Statement: [Central India, containsCity, Guna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guna Context triple: [Central India, containsCity, Guna]
-
A.
Guna
chosen
Guna is a city in the central Indian state of Madhya Pradesh known as an important regional administrative and commercial center.
-
B.
Gudakesha
Gudakesha is an epithet of the warrior prince Arjuna from the Indian epic Mahabharata, highlighting his mastery over sleep and unwavering focus.
-
C.
Yoogali
Yoogali is a small town in the Riverina region of New South Wales, Australia, known for its agricultural surroundings and proximity to the city of Griffith.
-
D.
Beni
Beni is a sparsely populated, largely Amazonian department in northeastern Bolivia known for its tropical lowlands, cattle ranching, and rich indigenous cultures.
-
E.
Beni
Beni is a city in the eastern Democratic Republic of the Congo that became internationally known as a major hotspot of conflict and public health crises, including serving as the epicenter of the 2018–2020 Kivu Ebola epidemic.
- 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_69a493b21f2881908132dcf45dcd2f36 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b415ac688190bbcef455935a3116 |
completed | March 1, 2026, 9:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac8f64ead48190be6f40e62b17bc12 |
completed | March 7, 2026, 8:49 p.m. |
Created at: March 1, 2026, 7:40 p.m.