Triple
T10987588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gush Dan |
E259669
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Ra'anana |
E567735
|
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: Ra'anana | Statement: [Gush Dan, hasMajorCity, Ra'anana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ra'anana Context triple: [Gush Dan, hasMajorCity, Ra'anana]
-
A.
Ra'anana
chosen
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.
-
B.
Netanya
Netanya is a coastal city in central Israel on the Mediterranean Sea, known for its beaches, tourism, and role as a regional economic center.
-
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.
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.
- 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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d787b3c8388190be77e95d56979a7f |
completed | April 9, 2026, 11:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e49677a2508190a113f9f24e318e48 |
completed | April 19, 2026, 8:46 a.m. |
Created at: April 8, 2026, 9:24 p.m.