Triple
T26190397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prahran railway station |
E654945
|
entity |
| Predicate | hasOpensideShelters |
P3789
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Prahran railway station, hasOpensideShelters, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOpensideShelters Context triple: [Prahran railway station, hasOpensideShelters, yes]
-
A.
hasShelters
chosen
Indicates that one entity provides, contains, or is associated with one or more shelters for another entity or purpose.
-
B.
hasHardenedShelters
Indicates that an entity possesses or is equipped with shelters that are reinforced or hardened for protection.
-
C.
numberOfPaintedShelters
Indicates the count of shelters that have been painted in the given context.
-
D.
areaServedAsShelterFor
Indicates that one entity functioned as a shelter or refuge for another entity, providing protection or a safe place.
-
E.
hasShelteredAreas
Indicates that one entity provides or contains areas that offer protection or cover for another 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_69ee5b469bc081908fe486453fdad810 |
completed | April 26, 2026, 6:36 p.m. |
| NER | Named-entity recognition | batch_69f6691f5e188190b12c7b2eb729a45e |
completed | May 2, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69f66598d6008190a7ca8ff80399fd34 |
completed | May 2, 2026, 8:59 p.m. |
Created at: April 26, 2026, 8:44 p.m.