Triple
T13968883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Binyamina |
E335999
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Hadera |
E563312
|
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: Hadera | Statement: [Binyamina, nearCity, Hadera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hadera Context triple: [Binyamina, nearCity, Hadera]
-
A.
Hadera
chosen
Hadera is a coastal city in northern Israel known for its power station, beaches, and location between Tel Aviv and Haifa.
-
B.
Herzliya
Herzliya is a coastal city in central Israel known as a high-tech and academic hub, home to major technology companies and institutions.
-
C.
Ramat Gan
Ramat Gan is a city in the Tel Aviv District of Israel, known for its diamond exchange district, business centers, and large urban park.
-
D.
Kiryat Ono
Kiryat Ono is a small suburban city in central Israel, located in the Tel Aviv metropolitan area.
-
E.
Ra'anana
Ra'anana is a prosperous suburban city in central Israel known for its high quality of life, strong education system, and significant high-tech and business presence.
- 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e8daeac8190aadd4b3b60222482 |
completed | April 14, 2026, 12:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee5da6a2081909dcc9785598e1196 |
completed | May 9, 2026, 7:44 a.m. |
Created at: April 9, 2026, 10:18 p.m.