Triple
T15957769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oryx beisa |
E386978
|
entity |
| Predicate | commonName |
P570
|
FINISHED |
| Object | beisa oryx |
E1181982
|
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: beisa oryx | Statement: [Oryx beisa, commonName, beisa oryx]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: beisa oryx Context triple: [Oryx beisa, commonName, beisa oryx]
-
A.
Beisa oryx
chosen
The Beisa oryx is a large, desert-adapted antelope native to arid regions of East Africa, recognized by its long, straight horns and striking facial markings.
-
B.
Scimitar oryx
The scimitar oryx is a large, desert-adapted antelope native to North Africa, distinguished by its long, curved horns and pale coat.
-
C.
Arabian oryx
The Arabian oryx is a desert-adapted antelope native to the Arabian Peninsula, known for its long straight horns and striking white coat.
-
D.
Oryx
Oryx is a mysterious and resilient woman in Margaret Atwood’s dystopian novel "Oryx and Crake," whose traumatic past and complex relationships profoundly shape the story’s emotional and ethical core.
-
E.
Oryx
Oryx is a genus of large, long-horned antelopes adapted to arid and desert environments in Africa and the Arabian Peninsula.
- 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_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156fc6f348190b49c4858281a0904 |
completed | April 16, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffeb82a29081909ef0e2685d0705c3 |
completed | May 10, 2026, 2:20 a.m. |
Created at: April 10, 2026, 4:53 a.m.