Triple
T7518746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sawu language |
E177711
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object | Sabu |
E247379
|
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: Sabu | Statement: [Sawu language, hasAlternativeName, Sabu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sabu Context triple: [Sawu language, hasAlternativeName, Sabu]
-
A.
Sabu
chosen
Sabu is an alternative name for the Shabo language, a little-documented and possibly language-isolate tongue spoken by a small community in southwestern Ethiopia.
-
B.
Sabu
Sabu is a pioneering hardcore professional wrestler best known for his extreme, high-risk style and influential run in ECW.
-
C.
Ganja
Ganja is one of Azerbaijan’s largest and oldest cities, known as a historic cultural and economic center in the South Caucasus.
-
D.
Sahl Hasheesh
Sahl Hasheesh is a modern Red Sea coastal resort town in Egypt known for its luxury hotels, beaches, and diving and snorkeling sites.
-
E.
Substiane
Substiane is a La Roche-Posay skincare line formulated to address loss of firmness, density, and comfort in mature or aging skin.
- 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_69c69f2891148190a484f3b8222c6f1b |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5f850c081909e697219071293fc |
completed | March 27, 2026, 9:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8462610b481909fa74023852b0154 |
completed | March 28, 2026, 9:20 p.m. |
Created at: March 27, 2026, 3:46 p.m.