Triple
T20430540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | President of Yemen |
E501117
|
entity |
| Predicate | seat |
P75
|
FINISHED |
| Object | Sanaʽa |
—
|
NE NERFINISHED |
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: Sanaʽa | Statement: [President of Yemen, seat, Sanaʽa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sanaʽa Context triple: [President of Yemen, seat, Sanaʽa]
-
A.
Sanaʽa
chosen
Sanaʽa is the historic capital and one of the largest cities of Yemen, renowned for its ancient architecture and cultural significance in the Arabian Peninsula.
-
B.
Sanaa
Sanaa is a table-service restaurant at Disney’s Animal Kingdom Lodge known for its African-inspired cuisine with Indian flavors and savanna views of roaming wildlife.
-
C.
Ma'rib
Ma'rib is an ancient city in present-day Yemen that served as the political and religious center of the Sabaean civilization, renowned for its monumental dam and role in South Arabian trade.
-
D.
Salalah
Salalah is a coastal city in southern Oman known for its monsoon-cooled climate, lush green landscapes, and role as a regional tourism and commercial hub.
-
E.
Zabid
Zabid is an ancient city in western Yemen renowned as a former political capital and a historic center of Islamic learning and culture.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4aa68fc8190b1a14c55575ef04a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67bad58448190be1563d74dbf9957 |
completed | April 20, 2026, 7:17 p.m. |
Created at: April 16, 2026, 11:31 a.m.