Triple
T2489434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King George Street, Jerusalem |
E52003
|
entity |
| Predicate | hasReligiousBuildingsNearby |
P29505
|
FINISHED |
| Object | synagogues |
—
|
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: synagogues | Statement: [King George Street, Jerusalem, hasReligiousBuildingsNearby, synagogues]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReligiousBuildingsNearby Context triple: [King George Street, Jerusalem, hasReligiousBuildingsNearby, synagogues]
-
A.
nearReligiousSite
chosen
Indicates that one entity is located close to or in the immediate vicinity of a religious site.
-
B.
hasNearbyCulturalBuilding
Indicates that one entity is located close to another entity that is a cultural building, such as a museum, theater, or gallery.
-
C.
hasPlaceOfWorship
Indicates that an entity possesses, contains, or is associated with a designated location used for religious or spiritual worship.
-
D.
hasReligiousSite
Indicates that a location or entity possesses, contains, or is associated with a religious site such as a temple, church, mosque, shrine, or similar place of worship.
-
E.
hasMosque
Indicates that one entity possesses, contains, or is the location of a mosque.
- 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_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. |
Created at: March 6, 2026, 9:45 p.m.