Triple
T35047912
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Praça de Almeida Garrett |
E1011251
|
entity |
| Predicate | servesAsForecourtOf |
P93277
|
FINISHED |
| Object | São Bento railway station |
—
|
NE NERFINISHED |
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: São Bento railway station | Statement: [Praça de Almeida Garrett, servesAsForecourtOf, São Bento railway station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesAsForecourtOf Context triple: [Praça de Almeida Garrett, servesAsForecourtOf, São Bento railway station]
-
A.
isForecourtOf
chosen
Indicates that one place or area serves as the forecourt (an open, typically frontal space or entrance area) of another place or structure.
-
B.
hasSportsFacilityRole
Indicates that an entity holds a specific role, function, or position associated with a sports facility.
-
C.
runwayAdjacentTo
Indicates that a runway is directly next to or alongside another feature or area, with no significant separation between them.
-
D.
BuckV.BellCourt
Indicates the court that issued the decision in the Buck v. Bell case.
-
E.
parkServed
Indicates that a particular park is provided with services or coverage by a specified entity (such as a transit line, facility, or administrative body).
- 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_69f76dcfdda48190b1ebae5da8b54f12 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69ffecdcbac4819093b725a7dbe0e61b |
completed | May 10, 2026, 2:26 a.m. |
| PD | Predicate disambiguation | batch_69ffec3633288190adbbd84e277708dc |
completed | May 10, 2026, 2:23 a.m. |
Created at: May 3, 2026, 4:01 p.m.