Triple
T13036889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Salem district |
E326583
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
Omalur
Omalur is a town in the Indian state of Tamil Nadu, situated near the city of Salem and functioning as a local administrative and commercial center for the surrounding region.
|
E1020699
|
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: Omalur | Statement: [Salem district, hasTown, Omalur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Omalur Context triple: [Salem district, hasTown, Omalur]
-
A.
Vandiyur
Vandiyur is a locality in Madurai, Tamil Nadu, known for its historic temple tank and religious significance.
-
B.
Vilayanur
Vilayanur is the given name of V. S. Ramachandran, a prominent neuroscientist known for his work on visual perception and phantom limbs.
-
C.
Hattiangadi
Hattiangadi is a village in the Kundapura region of Karnataka, India, known for its historic temples and coastal cultural heritage.
-
D.
Nannilam
Nannilam is a small town in the Tiruvarur district of Tamil Nadu, India, known for its traditional Tamil culture and rural setting.
-
E.
Kammala
Kammala was a historical figure known primarily as one of the children of Zhenjin, the Crown Prince of the Yuan dynasty and son of Kublai Khan.
- 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: Omalur Triple: [Salem district, hasTown, Omalur]
Generated description
Omalur is a town in the Indian state of Tamil Nadu, situated near the city of Salem and functioning as a local administrative and commercial center for the surrounding region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Omalur Target entity description: Omalur is a town in the Indian state of Tamil Nadu, situated near the city of Salem and functioning as a local administrative and commercial center for the surrounding region.
-
A.
Vandiyur
Vandiyur is a locality in Madurai, Tamil Nadu, known for its historic temple tank and religious significance.
-
B.
Vilayanur
Vilayanur is the given name of V. S. Ramachandran, a prominent neuroscientist known for his work on visual perception and phantom limbs.
-
C.
Hattiangadi
Hattiangadi is a village in the Kundapura region of Karnataka, India, known for its historic temples and coastal cultural heritage.
-
D.
Nannilam
Nannilam is a small town in the Tiruvarur district of Tamil Nadu, India, known for its traditional Tamil culture and rural setting.
-
E.
Kammala
Kammala was a historical figure known primarily as one of the children of Zhenjin, the Crown Prince of the Yuan dynasty and son of Kublai Khan.
- 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6d5fbaea8819080ca249159d6c125 |
completed | May 3, 2026, 4:58 a.m. |
| NEDg | Description generation | batch_69f6d943a80c81909bc39b9a9ef303bd |
completed | May 3, 2026, 5:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6da1e56388190b536831b2c6d493f |
completed | May 3, 2026, 5:16 a.m. |
Created at: April 9, 2026, 8:55 p.m.