Triple
T7795596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shamsuddin Ilyas Shah |
E180290
|
entity |
| Predicate | regionRuled |
P15936
|
FINISHED |
| Object | Satgaon |
E204084
|
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: Satgaon | Statement: [Shamsuddin Ilyas Shah, regionRuled, Satgaon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Satgaon Context triple: [Shamsuddin Ilyas Shah, regionRuled, Satgaon]
-
A.
Satgaon
chosen
Satgaon was a historically significant port city in the Bengal region, flourishing as a major center of trade and commerce under the Bengal Sultanate.
-
B.
Ghoghardiha
Ghoghardiha is a town located in the Madhubani district of the Indian state of Bihar.
-
C.
Sultanganj
Sultanganj is a town in Bihar, India, known as a significant Hindu pilgrimage site on the banks of the Ganges River.
-
D.
Gobardanga
Gobardanga is a town in the Indian state of West Bengal known for its suburban character and connectivity to Kolkata via the Sealdah–Bangaon railway line.
-
E.
Dharangaon
Dharangaon is a town in the Jalgaon district of Maharashtra, India, known for its agricultural surroundings and role as a local commercial center.
- 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_69ca827d22208190b4dc5aa680edcf5d |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cae94c41408190b73e37c0ff2c6628 |
completed | March 30, 2026, 9:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb13f866ac8190bca2b8477b62d7e4 |
completed | March 31, 2026, 12:23 a.m. |
Created at: March 30, 2026, 4:31 p.m.