Triple
T319577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Municipal Chamber of Albufeira |
E7782
|
entity |
| Predicate | hasSeatIn |
P3522
|
FINISHED |
| Object | city of Albufeira |
—
|
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: city of Albufeira | Statement: [Municipal Chamber of Albufeira, hasSeatIn, city of Albufeira]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeatIn Context triple: [Municipal Chamber of Albufeira, hasSeatIn, city of Albufeira]
-
A.
hasSeat
chosen
Indicates that one entity possesses, provides, or includes a seat for another entity.
-
B.
hasSeating
Indicates that one entity provides or contains seating capacity or seating arrangements for another entity.
-
C.
hasReservedSeats
Indicates that specific seats have been set aside or allocated in advance for a particular entity or purpose.
-
D.
hasClubSeats
Indicates that an entity (such as a venue or section) includes or is equipped with club-level seating.
-
E.
otherSeat
Indicates that one entity is the alternative or different seat relative to another seat in a given context.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea7edbc48190b9031bd1af48f72a |
completed | Feb. 28, 2026, 1:15 p.m. |
| PD | Predicate disambiguation | batch_69a2e946607081909c8b97473aaf8d1b |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.