Triple
T9365930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Irifiyen |
E225397
|
entity |
| Predicate | associatedWithCity |
P1481
|
FINISHED |
| Object | Imzouren |
E795337
|
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: Imzouren | Statement: [Irifiyen, associatedWithCity, Imzouren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Imzouren Context triple: [Irifiyen, associatedWithCity, Imzouren]
-
A.
Imzouren
chosen
Imzouren is a town in northern Morocco’s Rif region, situated near the Mediterranean coast close to the city of Al Hoceima.
-
B.
La Tzoumaz
La Tzoumaz is a Swiss alpine village and ski resort in the 4 Vallées region, known for its family-friendly slopes and access to extensive interconnected ski terrain.
-
C.
Zezuru
Zezuru is a major dialect of the Shona language spoken primarily in central and northern Zimbabwe.
-
D.
Zorzor
Zorzor is a town in northwestern Liberia that serves as one of the main urban centers of Lofa County.
-
E.
Zardoz
Zardoz is a 1974 science fiction film directed by John Boorman, known for its surreal, dystopian vision and starring Sean Connery in one of his most unconventional roles.
- 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_69ca842cbddc819099d71ecec48cf9e5 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd5041261c81909edab3b7fcd1fe22 |
completed | April 1, 2026, 5:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d100d3444c81908b14f165ec128b76 |
completed | April 4, 2026, 12:15 p.m. |
Created at: March 30, 2026, 7:43 p.m.