Triple
T3465281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Panipat |
E73121
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Textile City
Textile City is a nickname for Panipat, a major Indian hub renowned for its large-scale textile and handloom industry.
|
E358340
|
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: Textile City | Statement: [Panipat, nickname, Textile City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Textile City Context triple: [Panipat, nickname, Textile City]
-
A.
Textile City
Textile City is a nickname for Daegu, a major South Korean city historically known as a center of the textile and fashion industries.
-
B.
Textile City
Textile City is a popular nickname for Coimbatore, a major South Indian industrial hub renowned for its extensive textile and garment manufacturing industry.
-
C.
White City
White City is a small unincorporated community and census-designated place in Salt Lake County, Utah, primarily residential in character.
-
D.
White City
White City was the gleaming, neoclassical fairground of the 1893 World’s Columbian Exposition in Chicago, famed for its grand architecture and extensive use of electric lighting.
-
E.
Bricktown
Bricktown is a revitalized former warehouse district in downtown Oklahoma City known for its entertainment venues, restaurants, and canal-side attractions.
- 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: Textile City Triple: [Panipat, nickname, Textile City]
Generated description
Textile City is a nickname for Panipat, a major Indian hub renowned for its large-scale textile and handloom industry.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Textile City Target entity description: Textile City is a nickname for Panipat, a major Indian hub renowned for its large-scale textile and handloom industry.
-
A.
Textile City
Textile City is a nickname for Daegu, a major South Korean city historically known as a center of the textile and fashion industries.
-
B.
Textile City
Textile City is a popular nickname for Coimbatore, a major South Indian industrial hub renowned for its extensive textile and garment manufacturing industry.
-
C.
White City
White City was the gleaming, neoclassical fairground of the 1893 World’s Columbian Exposition in Chicago, famed for its grand architecture and extensive use of electric lighting.
-
D.
White City
White City is a small unincorporated community and census-designated place in Salt Lake County, Utah, primarily residential in character.
-
E.
Bricktown
Bricktown is a revitalized former warehouse district in downtown Oklahoma City known for its entertainment venues, restaurants, and canal-side attractions.
- 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_69ad85b224d481908ff8be51338d24ff |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb0f2d3881908a5fa871341564ed |
completed | March 8, 2026, 6:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3612720308190b5a0d943a754883f |
completed | March 13, 2026, 12:58 a.m. |
| NEDg | Description generation | batch_69b361c3f7508190aacfc24528546614 |
completed | March 13, 2026, 1 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3624afd088190883f14c1b17421af |
completed | March 13, 2026, 1:03 a.m. |
Created at: March 8, 2026, 3:17 p.m.