Triple
T19939296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vaiko |
E479260
|
entity |
| Predicate | representedConstituency |
P15002
|
FINISHED |
| Object |
Sivakasi
Sivakasi is a town in Tamil Nadu, India, widely known as a major center for the production of fireworks, safety matches, and printing.
|
E1403374
|
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: Sivakasi | Statement: [Vaiko, representedConstituency, Sivakasi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sivakasi Context triple: [Vaiko, representedConstituency, Sivakasi]
-
A.
Kallakurichi
Kallakurichi is a town in the Indian state of Tamil Nadu, known as an agricultural and commercial center in the region.
-
B.
Nannilam
Nannilam is a small town in the Tiruvarur district of Tamil Nadu, India, known for its traditional Tamil culture and rural setting.
-
C.
Keelakarai
Keelakarai is a coastal town in Tamil Nadu, India, known for its historic mosques, maritime trading heritage, and culturally rich Muslim community.
-
D.
Vedalam
Vedalam is a 2015 Tamil-language action film starring Ajith Kumar, known for its mass appeal, high-octane fight sequences, and emotional brother-sister storyline.
-
E.
Vandiyur
Vandiyur is a locality in Madurai, Tamil Nadu, known for its historic temple tank and religious significance.
- 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: Sivakasi Triple: [Vaiko, representedConstituency, Sivakasi]
Generated description
Sivakasi is a town in Tamil Nadu, India, widely known as a major center for the production of fireworks, safety matches, and printing.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sivakasi Target entity description: Sivakasi is a town in Tamil Nadu, India, widely known as a major center for the production of fireworks, safety matches, and printing.
-
A.
Kallakurichi
Kallakurichi is a town in the Indian state of Tamil Nadu, known as an agricultural and commercial center in the region.
-
B.
Nannilam
Nannilam is a small town in the Tiruvarur district of Tamil Nadu, India, known for its traditional Tamil culture and rural setting.
-
C.
Keelakarai
Keelakarai is a coastal town in Tamil Nadu, India, known for its historic mosques, maritime trading heritage, and culturally rich Muslim community.
-
D.
Vedalam
Vedalam is a 2015 Tamil-language action film starring Ajith Kumar, known for its mass appeal, high-octane fight sequences, and emotional brother-sister storyline.
-
E.
Vandiyur
Vandiyur is a locality in Madurai, Tamil Nadu, known for its historic temple tank and religious significance.
- 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_69d8e522a17c819095165d4d24939fd8 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65a19d77c819088bce99c94568d0d |
completed | April 20, 2026, 4:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07f6ec40f88190926f1330300fdc61 |
completed | May 16, 2026, 4:47 a.m. |
| NEDg | Description generation | batch_6a07f9c8f6c08190a949ebbb70b08d01 |
completed | May 16, 2026, 4:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07fb3a144c8190b9bf2148702d6b6c |
completed | May 16, 2026, 5:06 a.m. |
Created at: April 10, 2026, 1:53 p.m.