Triple
T4534638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aqua Line (Noida Metro connection) |
E106377
|
entity |
| Predicate | numberOfCorridorsInSystemAtOpening |
P4095
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Aqua Line (Noida Metro connection), numberOfCorridorsInSystemAtOpening, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCorridorsInSystemAtOpening Context triple: [Aqua Line (Noida Metro connection), numberOfCorridorsInSystemAtOpening, 1]
-
A.
numberOfCorridors
chosen
Indicates the total count of corridors associated with or contained within a given entity or structure.
-
B.
lengthOfCorridors
Indicates the measured extent or distance of corridors within a given space or structure.
-
C.
numberOfHalls
Indicates the quantity of halls associated with a given entity or location.
-
D.
hasNumberOfEntrances
Indicates the relationship that specifies how many entrances an entity possesses.
-
E.
hasCorridor
Indicates that one entity includes, is connected by, or provides access through a corridor to another entity.
- 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_69bd43f3d6e08190a91824f833d51bbe |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57a2301c8190aa59280a16750156 |
completed | March 20, 2026, 2:20 p.m. |
| PD | Predicate disambiguation | batch_69bd521edd00819099dfccaa65dddd61 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:04 p.m.