Triple
T27741916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green Line (Delhi Metro) |
E701875
|
entity |
| Predicate | connectsIndustrialAreas |
P114262
|
FINISHED |
| Object | Mundka industrial belt |
—
|
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: Mundka industrial belt | Statement: [Green Line (Delhi Metro), connectsIndustrialAreas, Mundka industrial belt]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsIndustrialAreas Context triple: [Green Line (Delhi Metro), connectsIndustrialAreas, Mundka industrial belt]
-
A.
connectsIndustrialCenter
Indicates a relationship where one entity serves as a link or route that joins or provides access between industrial centers.
-
B.
connectsToIndustrialArea
chosen
Indicates that one entity has a direct link, route, or access connection to an industrial area.
-
C.
containsIndustrialAreas
Indicates that one entity includes or encompasses industrial areas within its boundaries or scope.
-
D.
connectsCommercialAreas
Indicates a relationship where one entity links or provides direct access between two or more commercial areas or business districts.
-
E.
connectsArea
Indicates that one area serves as a link or passage between two other areas, enabling movement or interaction between them.
- 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_69ef6a53c7388190899baa6daf42301c |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69fcec5f8b448190b48330a19b462d24 |
completed | May 7, 2026, 7:47 p.m. |
| PD | Predicate disambiguation | batch_69fceaf1e23881908ca24160a638e329 |
completed | May 7, 2026, 7:41 p.m. |
Created at: April 27, 2026, 4:12 p.m.