Triple
T37802878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zichron Moshe neighborhood |
E942426
|
entity |
| Predicate | nearbyEntityType |
P193931
|
FINISHED |
| Object | other Haredi neighborhoods of Jerusalem |
—
|
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: other Haredi neighborhoods of Jerusalem | Statement: [Zichron Moshe neighborhood, nearbyEntityType, other Haredi neighborhoods of Jerusalem]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyEntityType Context triple: [Zichron Moshe neighborhood, nearbyEntityType, other Haredi neighborhoods of Jerusalem]
-
A.
hasNearbyEntityType
chosen
Indicates that an entity has at least one other entity of a specified type located within a defined nearby spatial or contextual range.
-
B.
nearbyTo
Indicates that one entity is located close in distance or position to another entity.
-
C.
nearbyFacilityType
Indicates that a facility of a specified type is located close to a given reference entity or location.
-
D.
nearbyUse
Indicates that one entity uses or operates another entity that is located nearby or in close physical proximity.
-
E.
nearbyLocation
Indicates that one location is situated close to another location in physical space.
- 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_69f76ee8104c8190ab17133ccd8f86e6 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69ff234f32888190a1d800a3bda432eb |
completed | May 9, 2026, 12:06 p.m. |
| PD | Predicate disambiguation | batch_69ff228ae9a0819083f4b97c10b923f4 |
completed | May 9, 2026, 12:03 p.m. |
Created at: May 3, 2026, 4:19 p.m.