Triple
T2596031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tel HaShomer |
E58232
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Kiryat Ono |
E158565
|
NE 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: Kiryat Ono | Statement: [Tel HaShomer, near, Kiryat Ono]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kiryat Ono Context triple: [Tel HaShomer, near, Kiryat Ono]
-
A.
Kiryat Ono
chosen
Kiryat Ono is a small suburban city in central Israel, located in the Tel Aviv metropolitan area.
-
B.
Kiryat Shmona
Kiryat Shmona is a northern Israeli city near the Lebanese border, known for its strategic location and frequent exposure to cross-border conflict.
-
C.
Kiryat Tiv'on
Kiryat Tiv'on is a town in northern Israel, near Haifa, known for its residential character and proximity to archaeological and natural sites.
-
D.
Givatayim
Givatayim is a small, densely populated city in Israel’s Tel Aviv metropolitan area, known for its residential character and proximity to major urban centers.
-
E.
Herzliya
Herzliya is a coastal city in central Israel known as a high-tech and academic hub, home to major technology companies and institutions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ab4ac14040819098b13f4a27d5c8ff |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd42978f881909f217e7ec9ac3144 |
completed | March 7, 2026, 7:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b055b28ad88190a9fafc15871afa5f |
completed | March 10, 2026, 5:32 p.m. |
Created at: March 6, 2026, 9:49 p.m.