Triple
T802496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wrocław |
E17157
|
entity |
| Predicate | twinCity |
P1072
|
FINISHED |
| Object | Ramat Gan |
E19003
|
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: Ramat Gan | Statement: [Wrocław, twinCity, Ramat Gan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ramat Gan Context triple: [Wrocław, twinCity, Ramat Gan]
-
A.
Ramat Gan
chosen
Ramat Gan is a city in the Tel Aviv District of Israel, known for its diamond exchange district, business centers, and large urban park.
-
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.
Rehovot
Rehovot is a city in central Israel known for its scientific and agricultural research institutions, including the Weizmann Institute of Science.
-
D.
Tel Aviv
Tel Aviv is a major Israeli coastal city known for its vibrant nightlife, high-tech industry, and modernist architecture.
-
E.
Yokneam Illit
Yokneam Illit is a city in northern Israel known for its high-tech industrial parks and rapid development from a small town into a regional technology hub.
- 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_69a49378b9c48190adbf5f62e5b7aca1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4aabd9fc081908ccadd8e8769de2d |
completed | March 1, 2026, 9:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac3b978de4819083b117ee3a6cb8c1 |
completed | March 7, 2026, 2:52 p.m. |
Created at: March 1, 2026, 7:38 p.m.