Triple
T16558164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biblical places |
E402262
|
entity |
| Predicate | hasExample |
P1259
|
FINISHED |
| Object | Sinai |
E6370
|
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: Sinai | Statement: [Biblical places, hasExample, Sinai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sinai Context triple: [Biblical places, hasExample, Sinai]
-
A.
Sinai
Sinai is one of the two small, historic funicular cars that operate on Los Angeles’ Angels Flight Railway.
-
B.
Sinait
Sinait is a coastal municipality in the province of Ilocos Sur in the Philippines, known for its religious pilgrimage sites and local salt-making industry.
-
C.
Sinai Peninsula
chosen
The Sinai Peninsula is a triangular land bridge between Africa and Asia, known for its desert landscapes, strategic location, and religious and historical significance.
-
D.
Sinajana
Sinajana is a small residential village located on the island of Guam in the western Pacific Ocean.
-
E.
מדבר סיני
מדבר סיני הוא חצי אי מדברי נרחב בצפון-מזרח מצרים, הידוע בנופיו ההרריים והצחיחים ובחשיבותו ההיסטורית והדתית ביהדות, נצרות ואסלאם.
- 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_69d8838648088190acf97ef11fc3f61b |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3576bce0c819087ab36f7dec5c394 |
completed | April 18, 2026, 10:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0067bcb698819092ede6ba4f8a4a2b |
completed | May 10, 2026, 11:10 a.m. |
Created at: April 10, 2026, 5:15 a.m.