Triple
T13813033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olympic House, Lausanne |
E331941
|
entity |
| Predicate | numberOfWorkstations |
P19443
|
FINISHED |
| Object | approximately 500 |
—
|
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: approximately 500 | Statement: [Olympic House, Lausanne, numberOfWorkstations, approximately 500]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfWorkstations Context triple: [Olympic House, Lausanne, numberOfWorkstations, approximately 500]
-
A.
hasComputerWorkstations
chosen
Indicates that an entity is equipped with or provides access to computer workstations.
-
B.
numberOfDesks
Indicates the quantity of desks associated with a given entity.
-
C.
numberOfTerminals
Indicates the total count of terminal points or endpoints associated with an entity.
-
D.
worksNumber
Indicates that an entity is identified or referenced by a specific works number, typically denoting a particular work, item, or production instance.
-
E.
numberOfOffices
Indicates the total count of offices associated with a given 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_69d81c59f8808190a851bc56afdc55e9 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de027198f8819095da3e714ac241f5 |
completed | April 14, 2026, 9:01 a.m. |
| PD | Predicate disambiguation | batch_69dbc862e9608190bd8a3d883959b7e4 |
completed | April 12, 2026, 4:29 p.m. |
Created at: April 9, 2026, 10:12 p.m.