Triple
T4959900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rang Panchami |
E111377
|
entity |
| Predicate | publicSpacesUsed |
P61443
|
FINISHED |
| Object | streets |
—
|
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: streets | Statement: [Rang Panchami, publicSpacesUsed, streets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: publicSpacesUsed Context triple: [Rang Panchami, publicSpacesUsed, streets]
-
A.
spaceUsage
Indicates how much physical or storage space is occupied or utilized by an entity relative to the total available space.
-
B.
totalSpace
Indicates the overall amount of space or capacity available or occupied by an entity or within a given context.
-
C.
hasPublicSpaceAlong
Indicates that a public space (such as a park, plaza, or walkway) is located adjacent to or runs alongside the referenced feature or element.
-
D.
hasPublicSpaces
Indicates that an entity includes or provides areas that are accessible and usable by the general public.
-
E.
numberOfParkingSpaces
Indicates the total count of parking spaces associated with a particular entity or location.
- F. None of above. chosen
Provenance (4 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_69bd4418390c8190b7e9766a2512ce55 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd72e49b048190bac55d9e7a6f7963 |
completed | March 20, 2026, 4:16 p.m. |
| PD | Predicate disambiguation | batch_69bd71447fe88190bb62c5e8753da7a7 |
completed | March 20, 2026, 4:09 p.m. |
| PDg | Predicate description generation | batch_69bd72e1b7cc8190b2e621fdf8f22e38 |
completed | March 20, 2026, 4:16 p.m. |
Created at: March 20, 2026, 1:32 p.m.