Triple
T7380039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Durg |
E170223
|
entity |
| Predicate | twinCityWith |
P1072
|
FINISHED |
| Object | Bhilai |
E208195
|
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: Bhilai | Statement: [Durg, twinCityWith, Bhilai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bhilai Context triple: [Durg, twinCityWith, Bhilai]
-
A.
Bhilai
chosen
Bhilai is an industrial city in central India best known for its large steel plant and planned urban infrastructure.
-
B.
Bhopal
Bhopal is the capital city of the Indian state of Madhya Pradesh, known for its historic lakes, administrative importance, and the site of the 1984 industrial disaster.
-
C.
Jabalpur
Jabalpur is a major city in the central Indian state of Madhya Pradesh, known for its industrial base, educational institutions, and nearby marble rock formations along the Narmada River.
-
D.
Korba
Korba is an industrial city in the Indian state of Chhattisgarh, known primarily for its coal mining and power generation industries.
-
E.
Korba
Korba is a coastal town in northeastern Tunisia known for its beaches, agriculture, 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_69c68a5d0ed08190b6d361e68f813330 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f1c61484819087874d4e7f9fd791 |
completed | March 27, 2026, 9:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c81ecb9b0481908af841456bce5430 |
completed | March 28, 2026, 6:32 p.m. |
Created at: March 27, 2026, 3:08 p.m.