Triple
T8773408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wayanad |
E208519
|
entity |
| Predicate | hasHeadquarters |
P62
|
FINISHED |
| Object |
Kalpetta
Kalpetta is a town in the Wayanad district of Kerala, India, known as an administrative and commercial center surrounded by scenic hills and plantations.
|
E760594
|
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: Kalpetta | Statement: [Wayanad, hasHeadquarters, Kalpetta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kalpetta Context triple: [Wayanad, hasHeadquarters, Kalpetta]
-
A.
Munnar
Munnar is a popular hill station in the Western Ghats of southern India, renowned for its sprawling tea plantations, cool climate, and scenic mountain landscapes.
-
B.
Panambi
Panambi is a city in southern Brazil known for its strong German-Brazilian cultural heritage and traditions.
-
C.
Kottayam
Kottayam is a prominent town in the Indian state of Kerala known as a major center of Syrian Christian heritage, education, and publishing.
-
D.
Kundapura
Kundapura is a coastal town in the Udupi district of Karnataka, India, known for its temples, beaches, and distinct regional culture.
-
E.
Jagdalpur
Jagdalpur is a city in the Bastar district of Chhattisgarh, India, known for its tribal culture, dense forests, and proximity to major waterfalls and national parks.
- 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: Kalpetta Triple: [Wayanad, hasHeadquarters, Kalpetta]
Generated description
Kalpetta is a town in the Wayanad district of Kerala, India, known as an administrative and commercial center surrounded by scenic hills and plantations.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kalpetta Target entity description: Kalpetta is a town in the Wayanad district of Kerala, India, known as an administrative and commercial center surrounded by scenic hills and plantations.
-
A.
Munnar
Munnar is a popular hill station in the Western Ghats of southern India, renowned for its sprawling tea plantations, cool climate, and scenic mountain landscapes.
-
B.
Panambi
Panambi is a city in southern Brazil known for its strong German-Brazilian cultural heritage and traditions.
-
C.
Kottayam
Kottayam is a prominent town in the Indian state of Kerala known as a major center of Syrian Christian heritage, education, and publishing.
-
D.
Kundapura
Kundapura is a coastal town in the Udupi district of Karnataka, India, known for its temples, beaches, and distinct regional culture.
-
E.
Jagdalpur
Jagdalpur is a city in the Bastar district of Chhattisgarh, India, known for its tribal culture, dense forests, and proximity to major waterfalls and national parks.
- 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_69ca835edb4481909b4aafb616dc5eb7 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f2dc87c8190be0597a260d95a0b |
completed | March 31, 2026, 11:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf88f4d35c81908de1fd713a5b2008 |
completed | April 3, 2026, 9:31 a.m. |
| NEDg | Description generation | batch_69cf8a8c87dc81909d5c0d769341b17c |
completed | April 3, 2026, 9:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cf8b79a0b48190a29491f5f8f81217 |
completed | April 3, 2026, 9:42 a.m. |
Created at: March 30, 2026, 6:41 p.m.