Triple
T28020754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Parks and Recreation Committee |
E707679
|
entity |
| Predicate | typeOfAreaCovered |
P6822
|
FINISHED |
| Object | urban parks |
—
|
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: urban parks | Statement: [Parks and Recreation Committee, typeOfAreaCovered, urban parks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfAreaCovered Context triple: [Parks and Recreation Committee, typeOfAreaCovered, urban parks]
-
A.
coveredArea
Indicates that one entity occupies or extends over a specific spatial region or surface area associated with another entity.
-
B.
mayCoverArea
Indicates that one entity is permitted or able to extend over, include, or encompass a specified spatial area.
-
C.
areaOf
Indicates that one entity represents the measured surface extent (area) of another entity.
-
D.
hasAreaType
chosen
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
E.
hasAreaRange
Indicates that something’s area falls within a specified minimum-to-maximum range.
- 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_69ef96baf3a881909a2b63844185dddd |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69fe349879848190bcd77e3cc3470458 |
completed | May 8, 2026, 7:08 p.m. |
| PD | Predicate disambiguation | batch_69fe31e3cf908190b23ebc2f7fe58722 |
completed | May 8, 2026, 6:56 p.m. |
Created at: April 27, 2026, 8:09 p.m.