Triple
T7364854
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Durgapur-Asansol industrial belt |
E169843
|
entity |
| Predicate | industrialCharacter |
P67736
|
FINISHED |
| Object | heavy industrialization |
—
|
LITERAL 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: heavy industrialization | Statement: [Durgapur-Asansol industrial belt, industrialCharacter, heavy industrialization]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: industrialCharacter Context triple: [Durgapur-Asansol industrial belt, industrialCharacter, heavy industrialization]
-
A.
industrialCategory
Indicates the industry or sector classification to which an entity (such as a business or organization) belongs.
-
B.
industrialFocus
chosen
Indicates a relationship where an entity is primarily concerned with, specialized in, or directed toward a particular industrial sector or area of industrial activity.
-
C.
industrializes
Indicates the process by which an entity transforms its economy or operations from primarily agrarian or manual production to large-scale, mechanized, and industrial production.
-
D.
workCharacter
Indicates that a person is a fictional or narrative character appearing in a particular creative work.
-
E.
workCharacterType
Indicates that a work involves or features a character of a specified type or role.
- F. None of above.
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_69c68a5ade988190885b7175f63b7534 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f26d6d6081909c7272a9ccae0d97 |
completed | March 27, 2026, 9:11 p.m. |
| PD | Predicate disambiguation | batch_69c6f02d36108190bcb34a95e6a30bd7 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:06 p.m.