Triple
T4001876
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muttrah Souq |
E89431
|
entity |
| Predicate | hasSection |
P35
|
FINISHED |
| Object | gold souq |
E342380
|
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: gold souq | Statement: [Muttrah Souq, hasSection, gold souq]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: gold souq Context triple: [Muttrah Souq, hasSection, gold souq]
-
A.
Gold Souq
chosen
Gold Souq is a traditional market area in Doha renowned for its numerous shops selling gold jewelry and other precious ornaments.
-
B.
Dubai Gold Souk
Dubai Gold Souk is a famous traditional market in Dubai renowned for its dense concentration of shops selling gold, jewelry, and precious stones.
-
C.
GOLD
GOLD is the radio call sign historically used by the British royal yacht HMY Britannia.
-
D.
Gold
Gold is a 2016 American crime adventure film in which Matthew McConaughey stars as a prospector chasing a potentially fraudulent gold discovery in the Indonesian jungle.
-
E.
Gold
Gold was the codename for one of the five Allied landing beaches used by British forces during the D-Day invasion of Normandy in World War II.
- 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_69aed9585e788190bec2d39deba3750f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefa4290a48190b9c8cffa012c4df1 |
completed | March 9, 2026, 4:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b54c5e95b88190a8b5234b18d7b769 |
completed | March 14, 2026, 11:54 a.m. |
Created at: March 9, 2026, 3:34 p.m.