Triple
T3997622
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kidal Region |
E87135
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Kidal |
E217450
|
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: Kidal | Statement: [Kidal Region, capital, Kidal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kidal Context triple: [Kidal Region, capital, Kidal]
-
A.
Kidal
chosen
Kidal is a remote desert town in northeastern Mali that serves as a key cultural and political center for Tuareg communities in the Adagh region.
-
B.
Negombo
Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
-
C.
Khondji
Khondji is the surname of Darius Khondji, a renowned cinematographer known for his visually distinctive work on international films.
-
D.
Garoua
Garoua is a major city in northern Cameroon that serves as an important commercial and administrative center and a key hub for river and overland transport in the region.
-
E.
Kumba
Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
- 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_69aed94118148190975e6aa4e554cde9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefa228d608190b936a86c98c92ef2 |
completed | March 9, 2026, 4:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b54c54f05c8190b18c2d4839a61b64 |
completed | March 14, 2026, 11:53 a.m. |
Created at: March 9, 2026, 3:34 p.m.