Triple
T9529396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nuweiba |
E229846
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Taba |
E43804
|
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: Taba | Statement: [Nuweiba, near, Taba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taba Context triple: [Nuweiba, near, Taba]
-
A.
Taba
chosen
Taba is a small Egyptian resort town on the Red Sea near the border with Israel, known for its beaches, coral reefs, and role as a popular gateway between the two countries.
-
B.
Qataban
Qataban was an ancient South Arabian kingdom known for its incense trade and strategic position along key caravan routes in what is now Yemen.
-
C.
Tabary
Tabary was a French comics artist best known for co-creating and illustrating the humorous series "Iznogoud" in collaboration with writer René Goscinny.
-
D.
Anseba
Anseba is a central region of Eritrea known for its diverse ethnic communities, agriculture, and the regional capital Keren.
-
E.
Buhera
Buhera is a rural town and district center in eastern Zimbabwe known for its agricultural activities and location within Manicaland Province.
- 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_69ca8479934c81908006d0e6e970ae05 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd98b1b93481909812245ac14e4988 |
completed | April 1, 2026, 10:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d14c30c6008190b2eff99d74f18070 |
completed | April 4, 2026, 5:36 p.m. |
Created at: March 30, 2026, 8 p.m.