Triple
T18837004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raisen district |
E460689
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Raisen town
Raisen town is an urban settlement in the Indian state of Madhya Pradesh, serving as a local administrative and commercial center within Raisen district.
|
E1347721
|
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: Raisen town | Statement: [Raisen district, contains, Raisen town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Raisen town Context triple: [Raisen district, contains, Raisen town]
-
A.
Rinku Town
Rinku Town is a commercial and entertainment district in Izumisano, Osaka Prefecture, known for its large outlet mall, seaside park, and proximity to Kansai International Airport.
-
B.
Landhi Town
Landhi Town was an administrative subdivision of Karachi, Pakistan, encompassing the industrial and residential area of Landhi.
-
C.
Ittehad Town
Ittehad Town is a residential neighborhood located in the SITE Town area of Karachi, Pakistan.
-
D.
Keda town
Keda town is a small urban center in southwestern Georgia that serves as the administrative and economic hub of Keda Municipality in the Adjara region.
-
E.
Tushingham
Tushingham is an English surname most notably associated with Rita Tushingham, a celebrated British actress known for her roles in 1960s cinema.
- 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: Raisen town Triple: [Raisen district, contains, Raisen town]
Generated description
Raisen town is an urban settlement in the Indian state of Madhya Pradesh, serving as a local administrative and commercial center within Raisen district.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Raisen town Target entity description: Raisen town is an urban settlement in the Indian state of Madhya Pradesh, serving as a local administrative and commercial center within Raisen district.
-
A.
Rinku Town
Rinku Town is a commercial and entertainment district in Izumisano, Osaka Prefecture, known for its large outlet mall, seaside park, and proximity to Kansai International Airport.
-
B.
Landhi Town
Landhi Town was an administrative subdivision of Karachi, Pakistan, encompassing the industrial and residential area of Landhi.
-
C.
Ittehad Town
Ittehad Town is a residential neighborhood located in the SITE Town area of Karachi, Pakistan.
-
D.
Keda town
Keda town is a small urban center in southwestern Georgia that serves as the administrative and economic hub of Keda Municipality in the Adjara region.
-
E.
Tushingham
Tushingham is an English surname most notably associated with Rita Tushingham, a celebrated British actress known for her roles in 1960s cinema.
- 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_69d8dcfa11e4819090ab1ef5bdcd2b2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5a99e86388190957acaaab401b5cb |
completed | April 20, 2026, 4:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0582a3bd7881908dcd97ece5f576a8 |
completed | May 14, 2026, 8:06 a.m. |
| NEDg | Description generation | batch_6a0584717b3c81908049e6590f7f96ec |
completed | May 14, 2026, 8:14 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a05852c2bbc8190ae7b8658817b5972 |
completed | May 14, 2026, 8:17 a.m. |
Created at: April 10, 2026, 11:56 a.m.