Triple
T34466752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DIBA |
E884790
|
entity |
| Predicate | hasOfficialBuildingIn |
P185687
|
FINISHED |
| Object | Barcelona |
—
|
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: Barcelona | Statement: [DIBA, hasOfficialBuildingIn, Barcelona]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOfficialBuildingIn Context triple: [DIBA, hasOfficialBuildingIn, Barcelona]
-
A.
hasHeadquartersBuilding
Indicates that an organization possesses a specific building that serves as its headquarters location.
-
B.
hasOfficeBuildings
Indicates that one entity possesses, controls, or is associated with one or more office buildings.
-
C.
hasGovernmentBuildingType
Indicates that a government building is classified as having a specific type or category of governmental function or use.
-
D.
hasMunicipalBuildings
Indicates that a place or jurisdiction possesses one or more buildings used for municipal or local government functions.
-
E.
isMajorOfficeBuildingIn
Indicates that a building is a primary or significant office structure located within a specified geographic or administrative area.
- 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_69f349c73a94819094dfcf50d00620b8 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7c33d59808190b647989a093f3488 |
completed | May 3, 2026, 9:50 p.m. |
| PD | Predicate disambiguation | batch_69f7c1b6e7a881908deb96bedb2713f4 |
completed | May 3, 2026, 9:44 p.m. |
| PDg | Predicate description generation | batch_69f7c29cf36481908e472d4dcb5573b9 |
completed | May 3, 2026, 9:48 p.m. |
Created at: May 1, 2026, 2:01 a.m.