Triple
T24978538
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Francisco Supervisorial District 2 |
E625094
|
entity |
| Predicate | hasLocalIssues |
P160831
|
FINISHED |
| Object | transportation and parking |
—
|
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: transportation and parking | Statement: [San Francisco Supervisorial District 2, hasLocalIssues, transportation and parking]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLocalIssues Context triple: [San Francisco Supervisorial District 2, hasLocalIssues, transportation and parking]
-
A.
hasLocalIssues
chosen
Indicates that an entity is associated with or affected by specific issues or problems occurring within a particular local area or community.
-
B.
hasIssueWith
Indicates that one entity experiences a problem, conflict, or concern related to another entity.
-
C.
hasOngoingIssues
Indicates that an entity is currently experiencing unresolved or continuing problems or difficulties.
-
D.
hasRecentIssue
Indicates that an entity is associated with an issue or problem that has occurred within a recent or specified time frame.
-
E.
hasInternalIssue
Indicates that an entity is experiencing a problem, fault, or malfunction originating within itself or its internal components or processes.
- 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_69e2ff254570819093d197b1900305ac |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f60c3b09488190ade1b69ff7f0df0e |
completed | May 2, 2026, 2:37 p.m. |
| PD | Predicate disambiguation | batch_69f60b8461ac81908c5bd3d73eed59f4 |
completed | May 2, 2026, 2:34 p.m. |
Created at: April 18, 2026, 6:02 a.m.