Triple
T28122026
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mayur Vihar Phase 1 metro station |
E710817
|
entity |
| Predicate | hasEntryExitGates |
P1973
|
FINISHED |
| Object | multiple |
—
|
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: multiple | Statement: [Mayur Vihar Phase 1 metro station, hasEntryExitGates, multiple]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEntryExitGates Context triple: [Mayur Vihar Phase 1 metro station, hasEntryExitGates, multiple]
-
A.
hasEntranceControl
Indicates that an entity implements or is subject to mechanisms that regulate or control access to its entrance.
-
B.
hasNumberOfEntrances
Indicates the relationship that specifies how many entrances an entity possesses.
-
C.
hasFaregates
chosen
Indicates that an entity is equipped with or contains faregates used to control or validate access, typically for paid entry.
-
D.
hasGatesFor
Indicates that one entity is equipped with or provides access through one or more gates intended for another entity.
-
E.
hasNumberOfExits
Indicates the relationship that specifies how many exits are associated with a given 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_69ef9b73bd288190a21ae3d6aa14f386 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69f6c49627908190b3553474c7c3072b |
completed | May 3, 2026, 3:44 a.m. |
| PD | Predicate disambiguation | batch_69f6c3f23ae081909a52801266063a3c |
completed | May 3, 2026, 3:41 a.m. |
Created at: April 27, 2026, 9:17 p.m.