Triple
T3014853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eliyahu Sasson |
E82311
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Tel Aviv |
E11499
|
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: Tel Aviv | Statement: [Eliyahu Sasson, workLocation, Tel Aviv]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tel Aviv Context triple: [Eliyahu Sasson, workLocation, Tel Aviv]
-
A.
Tel Aviv
chosen
Tel Aviv is a major Israeli coastal city known for its vibrant nightlife, high-tech industry, and modernist architecture.
-
B.
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.
-
C.
Tel Aviv metropolitan area
The Tel Aviv metropolitan area is Israel’s largest urban and economic hub, centered on the city of Tel Aviv and encompassing numerous surrounding municipalities along the Mediterranean coast.
-
D.
Petah Tikva
Petah Tikva is a major city in central Israel, known as one of the country’s oldest modern Jewish settlements and a significant industrial and commercial hub in the Tel Aviv metropolitan area.
-
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_69ad8b1eb53481908c39bbcd1ec104b2 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a69e8148190a97507740c9d26a8 |
completed | March 8, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1de87779c8190ae6833b80d34f5b2 |
completed | March 11, 2026, 9:28 p.m. |
Created at: March 8, 2026, 3 p.m.