Triple
T11444634
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alwar district |
E271231
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Kathumar
Kathumar is a town in the Alwar district of Rajasthan, India, known as a local administrative and market center in the region.
|
E926305
|
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: Kathumar | Statement: [Alwar district, contains, Kathumar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kathumar Context triple: [Alwar district, contains, Kathumar]
-
A.
Buhera
Buhera is a rural town and district center in eastern Zimbabwe known for its agricultural activities and location within Manicaland Province.
-
B.
Kumba
Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
-
C.
Kumba
Kumba is a major town in southwestern Cameroon known as a commercial hub and cultural crossroads where languages like Cameroonian Pidgin English are widely used.
-
D.
Kuda Huraa
Kuda Huraa is a small resort island in the Maldives, known for its luxury accommodations, white-sand beaches, and vibrant coral reefs.
-
E.
Kepez
Kepez is a populous district and municipality within the city of Antalya in southern Turkey, known for its residential areas and growing urban infrastructure.
- 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: Kathumar Triple: [Alwar district, contains, Kathumar]
Generated description
Kathumar is a town in the Alwar district of Rajasthan, India, known as a local administrative and market center in the region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kathumar Target entity description: Kathumar is a town in the Alwar district of Rajasthan, India, known as a local administrative and market center in the region.
-
A.
Buhera
Buhera is a rural town and district center in eastern Zimbabwe known for its agricultural activities and location within Manicaland Province.
-
B.
Kumba
Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
-
C.
Kumba
Kumba is a major town in southwestern Cameroon known as a commercial hub and cultural crossroads where languages like Cameroonian Pidgin English are widely used.
-
D.
Kuda Huraa
Kuda Huraa is a small resort island in the Maldives, known for its luxury accommodations, white-sand beaches, and vibrant coral reefs.
-
E.
Kepez
Kepez is a populous district and municipality within the city of Antalya in southern Turkey, known for its residential areas and growing urban infrastructure.
- 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_69d6aadff8888190a13f253f0d460874 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8088a66f48190b2b4a56cd62097cf |
completed | April 9, 2026, 8:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5d3afe7fc8190acc8c803ff5efe5a |
completed | April 20, 2026, 7:20 a.m. |
| NEDg | Description generation | batch_69e5d5cac9108190b7756329bfa320d3 |
completed | April 20, 2026, 7:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e5d7fd235081909870476cbc9817b2 |
completed | April 20, 2026, 7:38 a.m. |
Created at: April 8, 2026, 9:35 p.m.