Triple
T11213839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Silvassa |
E265378
|
entity |
| Predicate | nearestMajorCity |
P1982
|
FINISHED |
| Object | Surat |
E62832
|
NE FINISHED |
How this triple was built (2 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: Surat | Statement: [Silvassa, nearestMajorCity, Surat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Surat Context triple: [Silvassa, nearestMajorCity, Surat]
-
A.
Surat
chosen
Surat is a historic port city in the Indian state of Gujarat that became an important center of trade and commerce during the Mughal and early colonial periods.
-
B.
Ahmedabad
Ahmedabad is a major city in the western Indian state of Gujarat, known for its rich history, textile industry, and role as an important economic and cultural center.
-
C.
Aurangabad
Aurangabad is a historic city in the Indian state of Maharashtra, known for its rich cultural heritage and proximity to UNESCO World Heritage Sites like the Ajanta and Ellora Caves.
-
D.
Solapur
Solapur is a prominent city in southwestern Maharashtra, India, known for its textile industry, religious sites, and cultural significance to Marathi-speaking people.
-
E.
Vadodara
Vadodara is a major city in western India known for its rich cultural heritage, educational institutions, and industrial development.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6aac59460819089b9848b27f57848 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8d7f47c8190b78c640ff1a01943 |
completed | April 9, 2026, 5:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e497569efc8190b8e9cb6b1db3f94d |
completed | April 19, 2026, 8:50 a.m. |
Created at: April 8, 2026, 9:30 p.m.