Triple
T4542813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sitra |
E107574
|
entity |
| Predicate | connectedTo |
P37
|
FINISHED |
| Object | Manama |
E360013
|
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: Manama | Statement: [Sitra, connectedTo, Manama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manama Context triple: [Sitra, connectedTo, Manama]
-
A.
Manama
chosen
Manama is a small inland exclave of the Emirate of Ajman in the United Arab Emirates, known for its agricultural land and scattered settlements.
-
B.
Manama
Manama is the capital and largest city of Bahrain, serving as a key financial and commercial hub in the Persian Gulf region.
-
C.
Muscat
Muscat is the capital and largest city of Oman, known for its historic port, white-washed architecture, and strategic location on the Gulf of Oman.
-
D.
Muscat
Muscat is a highly aromatic family of grape varieties known for producing sweet, floral, and musky wines in many wine regions around the world.
-
E.
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.
- 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_69bd43f922788190b7edfa294e39b178 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57d3be988190bf118c4a87415613 |
completed | March 20, 2026, 2:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdc55ae8248190acda4f10eb5ce2e7 |
completed | March 20, 2026, 10:08 p.m. |
Created at: March 20, 2026, 1:05 p.m.