Triple
T8886119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buenos Aires Ecobici bike lanes |
E211534
|
entity |
| Predicate | socialBenefit |
P487
|
FINISHED |
| Object | increased accessibility to mobility |
—
|
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: increased accessibility to mobility | Statement: [Buenos Aires Ecobici bike lanes, socialBenefit, increased accessibility to mobility]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: socialBenefit Context triple: [Buenos Aires Ecobici bike lanes, socialBenefit, increased accessibility to mobility]
-
A.
benefits
chosen
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or action.
-
B.
benefice
Indicates that one entity grants or bestows a benefit, favor, or advantage upon another.
-
C.
welfareProgram
Indicates that an entity is involved in, provides, or is covered by a government or organizational welfare assistance program.
-
D.
welfareSupportFeature
Indicates that an entity provides, enables, or is associated with some form of welfare-related assistance or support.
-
E.
sectorBenefited
Indicates that a particular sector gains advantage, support, or positive impact from a given action, policy, resource, or 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_69ca838f9e20819096ab1f236a70381a |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc618bd30881909e54d0708f144786 |
completed | April 1, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69cc5c2aec04819093c932fe51c0f08d |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:53 p.m.