Triple
T2489432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King George Street, Jerusalem |
E52003
|
entity |
| Predicate | hasRestaurantsAndCafes |
P40355
|
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, hasRestaurantsAndCafes, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRestaurantsAndCafes Context triple: [King George Street, Jerusalem, hasRestaurantsAndCafes, yes]
-
A.
hasCafes
Indicates that one entity possesses, contains, or includes one or more cafes within it.
-
B.
hasRestaurant
Indicates that one entity possesses, operates, or contains a restaurant associated with it.
-
C.
isDiningDestination
Indicates that a place serves as a destination where people go specifically to eat meals or dine.
-
D.
numberOfRestaurantsAndRetail
Indicates the total count of entities that are either restaurants or retail establishments associated with a given subject.
-
E.
hasCharacterDining
Indicates that an entity offers or includes dining experiences where guests can eat while interacting with costumed characters.
- 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_69abd20b6d008190acec0eb172e218c9 |
completed | March 7, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69abd0b7cf088190bcff4dac6150044c |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd209d934819093600889af9104c3 |
completed | March 7, 2026, 7:21 a.m. |
Created at: March 6, 2026, 9:45 p.m.