Triple
T15851063
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Measure R (Los Angeles County) |
E384337
|
entity |
| Predicate | hasLocalReturnComponent |
P120761
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Measure R (Los Angeles County), hasLocalReturnComponent, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLocalReturnComponent Context triple: [Measure R (Los Angeles County), hasLocalReturnComponent, true]
-
A.
hasLocalExpression
Indicates that something has a specific form, representation, or manifestation that is valid or defined only within a particular local context or region.
-
B.
hasLocalContext
Indicates that something exists or occurs within a specific, surrounding situational or environmental context tied to a particular place or scope.
-
C.
hasSubcomponent
Indicates that one entity is a constituent part or component of another, larger entity.
-
D.
hasLocalLevel
Indicates that one entity possesses, is associated with, or is defined at a specific local administrative or organizational level relative to another entity.
-
E.
hasComponentProgram
Indicates that one program includes or is composed of another program as a component or sub-program.
- 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_69d86da422088190aac39e32e6c68429 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e174de2cd48190ab18e48c9f051a2a |
completed | April 16, 2026, 11:46 p.m. |
| PD | Predicate disambiguation | batch_69e142b976c081908d3ba3e705419f3a |
completed | April 16, 2026, 8:12 p.m. |
| PDg | Predicate description generation | batch_69e174da2c2c819099ec46616798245a |
completed | April 16, 2026, 11:46 p.m. |
Created at: April 10, 2026, 4:50 a.m.