Triple
T20169398
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guelmim-Oued Noun |
E491915
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Tan-Tan |
—
|
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: Tan-Tan | Statement: [Guelmim-Oued Noun, hasCity, Tan-Tan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tan-Tan Context triple: [Guelmim-Oued Noun, hasCity, Tan-Tan]
-
A.
Tan-Tan
chosen
Tan-Tan is a town in southwestern Morocco known as a gateway to the Sahara and for its annual cultural festival celebrating Sahrawi and nomadic heritage.
-
B.
Tin Tan
Tin Tan was a hugely popular Mexican actor, comedian, and singer, best known for his pachuco persona and influential roles in the Golden Age of Mexican cinema.
-
C.
Tomomi
Tomomi is a Japanese given name that can be used for people of any gender.
-
D.
Takkaze
Takkaze is a river in northern Ethiopia that flows through deep gorges before joining the Atbarah River, ultimately contributing to the Nile basin.
-
E.
Yan-yan
Yan-yan is the given name of Hung Yan-yan, a Hong Kong martial artist, actor, and action choreographer known for his work in kung fu and action cinema.
- 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_69da6266c6888190bc1a3ecf24814d34 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66846f4ec81908b0dc6a6e0ec27dd |
completed | April 20, 2026, 5:54 p.m. |
Created at: April 11, 2026, 11:35 p.m.