Triple
T17154079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saho language |
E416296
|
entity |
| Predicate | alternativeName |
P39
|
FINISHED |
| Object | Saho |
E92654
|
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: Saho | Statement: [Saho language, alternativeName, Saho]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saho Context triple: [Saho language, alternativeName, Saho]
-
A.
Saho
chosen
Saho is a Cushitic language spoken primarily by the Saho people in Eritrea and neighboring regions of the Horn of Africa.
-
B.
Saaho
Saaho is a 2019 Indian action thriller film known for its high-budget production, elaborate action sequences, and starring Prabhas in the lead role.
-
C.
Marichi
Marichi is a revered Vedic sage (one of the Saptarishi) regarded as a mind-born son of Brahma and an important progenitor in Hindu cosmology.
-
D.
Takabisha
Takabisha is a record-breaking steel roller coaster in Japan renowned for its extremely steep drop and intense thrill elements.
-
E.
Yasaq
Yasaq is the traditional legal code attributed to Genghis Khan that governed the Mongol Empire’s military, social, and political life.
- 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_69d886d279c081909f8ff1f743ddeb69 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f4092c40819096359ff90af16c3e |
completed | April 18, 2026, 9:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01415f3cd481908e96ca294cf3b247 |
completed | May 11, 2026, 2:39 a.m. |
Created at: April 10, 2026, 5:37 a.m.