Triple
T293121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Israel |
E6036
|
entity |
| Predicate | borderCountry |
P224
|
FINISHED |
| Object | Jordan |
E11658
|
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: Jordan | Statement: [Israel, borderCountry, Jordan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jordan Context triple: [Israel, borderCountry, Jordan]
-
A.
Jordan
chosen
Jordan is a Middle Eastern country located at the crossroads of Asia, Africa, and Europe, known for its ancient archaeological sites like Petra and its strategic political role in the region.
-
B.
Samaria
Samaria is a historical region in the central highlands of ancient Israel and the West Bank, known as the capital area of the northern Kingdom of Israel and a significant biblical and archaeological site.
-
C.
Canaan
Canaan is the ancient Near Eastern land traditionally associated with the biblical Promised Land, encompassing parts of modern-day Israel, Palestine, Lebanon, and surrounding areas.
-
D.
Syria
Syria is a country in the Eastern Mediterranean region of Western Asia, known for its ancient civilizations, diverse cultural heritage, and protracted civil war since 2011.
-
E.
Nasar
Nasar is a surname most notably associated with Sylvia Nasar, the economist and author of "A Beautiful Mind."
- 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_69a2e79114b081909490b3bf5a5dbb51 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2e976f32081908042485c4530e1e2 |
completed | Feb. 28, 2026, 1:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3c8b480388190b5f7f9e11479de91 |
completed | March 1, 2026, 5:03 a.m. |
Created at: Feb. 28, 2026, 1:06 p.m.