Triple
T6993986
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dortmund |
E162155
|
entity |
| Predicate | twinCity |
P1072
|
FINISHED |
| Object | Netanya |
E445634
|
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: Netanya | Statement: [Dortmund, twinCity, Netanya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Netanya Context triple: [Dortmund, twinCity, Netanya]
-
A.
Netanya
chosen
Netanya is a coastal city in central Israel on the Mediterranean Sea, known for its beaches, tourism, and role as a regional economic center.
-
B.
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.
-
C.
Ness Ziona
Ness Ziona is a small city in central Israel known for its scientific research institutions and proximity to Tel Aviv.
-
D.
Hadera
Hadera is a coastal city in northern Israel known for its power station, beaches, and location between Tel Aviv and Haifa.
-
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_69c68856d7808190ab33ee914640281b |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dbeaa88c8190a49f8504c1793e1f |
completed | March 27, 2026, 7:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7ad70f09881909f1a03f295486942 |
completed | March 28, 2026, 10:29 a.m. |
Created at: March 27, 2026, 2:32 p.m.