Triple
T16019169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Place de la Sorbonne |
E388552
|
entity |
| Predicate | hasBuildingUseAround |
P19783
|
FINISHED |
| Object | university buildings |
—
|
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: university buildings | Statement: [Place de la Sorbonne, hasBuildingUseAround, university buildings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBuildingUseAround Context triple: [Place de la Sorbonne, hasBuildingUseAround, university buildings]
-
A.
hasNearbyLandUse
chosen
Indicates that one land area is located close to another area characterized by a specific type of land use.
-
B.
hasBuildingUseAlongStreet
Indicates that a building located along a particular street is used for a specified purpose or function in relation to that street.
-
C.
hasNeighboringBuilding
Indicates that one building is located adjacent to or directly next to another building.
-
D.
hasMainBuildingNear
Indicates that the primary or central building associated with an entity is located in close physical proximity to another specified entity or place.
-
E.
hasBuildingStyleInSurroundings
Indicates that an entity is surrounded by or located in an area characterized by a particular building style.
- 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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1858a00888190b8505071575dc56f |
completed | April 17, 2026, 12:57 a.m. |
| PD | Predicate disambiguation | batch_69e1826a4f7c8190aba6d4f1075141b0 |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 4:55 a.m.