Triple
T30799230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lidra Caddesi |
E784318
|
entity |
| Predicate | hasPedestrianizationStatus |
P46481
|
FINISHED |
| Object | largely pedestrianized |
—
|
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: largely pedestrianized | Statement: [Lidra Caddesi, hasPedestrianizationStatus, largely pedestrianized]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPedestrianizationStatus Context triple: [Lidra Caddesi, hasPedestrianizationStatus, largely pedestrianized]
-
A.
hasPedestrianZoneStatus
chosen
Indicates whether an area or segment is designated as a pedestrian-only or pedestrian-priority zone and its corresponding status.
-
B.
hasPedestrianisationProjects
Indicates that an entity is involved in or associated with projects aimed at restricting or redesigning areas for pedestrian use.
-
C.
pedestrianizedSince
Indicates that an area or street has been designated for pedestrian use only starting from a specific point in time.
-
D.
pedestrianizedAreas
Indicates areas where vehicle traffic is restricted or removed so that pedestrians have priority use of the space.
-
E.
hasPedestrianAccessTo
Indicates that a location or area can be reached or entered safely and directly by people on foot.
- 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_69f224b3a7ec819096939414d103e31e |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fcdf2394748190b35cead3e208447d |
completed | May 7, 2026, 6:51 p.m. |
| PD | Predicate disambiguation | batch_69fcdbe344ec8190a0471911952f4b82 |
completed | May 7, 2026, 6:37 p.m. |
Created at: April 29, 2026, 8:42 p.m.