Triple
T19447749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rayagada district |
E486525
|
entity |
| Predicate | majorTown |
P316
|
FINISHED |
| Object |
Gunupur
Gunupur is a prominent town in the Rayagada district of Odisha, India, known as a local hub for education, commerce, and regional connectivity.
|
E1377880
|
NE FINISHED |
How this triple was built (4 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: Gunupur | Statement: [Rayagada district, majorTown, Gunupur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gunupur Context triple: [Rayagada district, majorTown, Gunupur]
-
A.
Gadarpur
Gadarpur is a town in the Udham Singh Nagar district of Uttarakhand, India, known primarily as an agricultural and trading center in the Terai region.
-
B.
Santalpur
Santalpur is a small town in the Patan district of Gujarat, India, known primarily as a local administrative and trading center for surrounding rural areas.
-
C.
Karanpur
Karanpur is a town located in the Ganganagar district of the northern Indian state of Rajasthan.
-
D.
Vikrampur
Vikrampur was a historic urban and political center in the Bengal region, renowned as an important seat of power and culture in medieval South Asia.
-
E.
Samastipur
Samastipur is a city in the Indian state of Bihar known as an important railway junction and agricultural trade center in the region.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Gunupur Triple: [Rayagada district, majorTown, Gunupur]
Generated description
Gunupur is a prominent town in the Rayagada district of Odisha, India, known as a local hub for education, commerce, and regional connectivity.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gunupur Target entity description: Gunupur is a prominent town in the Rayagada district of Odisha, India, known as a local hub for education, commerce, and regional connectivity.
-
A.
Gadarpur
Gadarpur is a town in the Udham Singh Nagar district of Uttarakhand, India, known primarily as an agricultural and trading center in the Terai region.
-
B.
Santalpur
Santalpur is a small town in the Patan district of Gujarat, India, known primarily as a local administrative and trading center for surrounding rural areas.
-
C.
Karanpur
Karanpur is a town located in the Ganganagar district of the northern Indian state of Rajasthan.
-
D.
Vikrampur
Vikrampur was a historic urban and political center in the Bengal region, renowned as an important seat of power and culture in medieval South Asia.
-
E.
Samastipur
Samastipur is a city in the Indian state of Bihar known as an important railway junction and agricultural trade center in the region.
- F. None of above. chosen
Provenance (5 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_69d8e8d7ad488190a3373045029b0f3b |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6338b25d88190bc137a411576c73f |
completed | April 20, 2026, 2:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a074047fcbc81908c969f0690300b57 |
completed | May 15, 2026, 3:48 p.m. |
| NEDg | Description generation | batch_6a07410d92708190b376e646a0ec8e74 |
completed | May 15, 2026, 3:51 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0741873b108190b540b0ae6b6b55d3 |
completed | May 15, 2026, 3:53 p.m. |
Created at: April 10, 2026, 1:38 p.m.