Triple
T27124050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Boulevard Light Rail Facility |
E687073
|
entity |
| Predicate | hasMaintenanceShops |
P195488
|
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: [South Boulevard Light Rail Facility, hasMaintenanceShops, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMaintenanceShops Context triple: [South Boulevard Light Rail Facility, hasMaintenanceShops, yes]
-
A.
hasShopsOn
Indicates that one entity (typically a street, area, or building) contains or is lined with shops located on or along it.
-
B.
hasIndependentShops
Indicates that an entity contains or is associated with retail businesses that operate independently rather than as part of large chains or franchises.
-
C.
hasShop
Indicates that one entity owns, operates, or is associated with a shop or retail establishment.
-
D.
hasOnlineShop
Indicates that an entity operates or is associated with a shop that sells goods or services via the internet.
-
E.
hasBodyShop
Indicates that one entity operates, owns, or is associated with an auto body repair shop.
- 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_69ef148c2b588190afc15b529f7af845 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69fdd5fba5048190b7d430ae2054a1fd |
completed | May 8, 2026, 12:24 p.m. |
| PD | Predicate disambiguation | batch_69fdd35f76f88190a1854ea27132f9c7 |
completed | May 8, 2026, 12:13 p.m. |
| PDg | Predicate description generation | batch_69fdd5faea908190a17d77e050362ef5 |
completed | May 8, 2026, 12:24 p.m. |
Created at: April 27, 2026, 9 a.m.