Triple
T2489433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King George Street, Jerusalem |
E52003
|
entity |
| Predicate | hasOfficeBuildings |
P39751
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [King George Street, Jerusalem, hasOfficeBuildings, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOfficeBuildings Context triple: [King George Street, Jerusalem, hasOfficeBuildings, yes]
-
A.
publicBuilding
Indicates that a building is designated for public use or access, typically serving communal, governmental, or civic functions.
-
B.
hasHeadquartersBuilding
Indicates that an organization possesses a specific building that serves as its headquarters location.
-
C.
hasTerminalBuildings
Indicates that one entity possesses or includes terminal buildings associated with it.
-
D.
hasOfficeType
Indicates that an entity’s office is classified as a specific type or category of office.
-
E.
hasOffice
Indicates that an entity possesses or maintains an office at a particular location or within a specific organization.
- 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_69ab4955111c8190835bf619adec21ff |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd17b7a048190bcc8f0a66514a052 |
completed | March 7, 2026, 7:19 a.m. |
| PD | Predicate disambiguation | batch_69abd0b980b481908d4932bcea4a6167 |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd1318f7881908a8fc42943df4879 |
completed | March 7, 2026, 7:18 a.m. |
Created at: March 6, 2026, 9:45 p.m.