Triple
T17324849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cape buffalo |
E420659
|
entity |
| Predicate | genus |
P87
|
FINISHED |
| Object | Syncerus |
E393957
|
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: Syncerus | Statement: [Cape buffalo, genus, Syncerus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Syncerus Context triple: [Cape buffalo, genus, Syncerus]
-
A.
Syncerus
chosen
Syncerus is a genus of large African bovids best known for the African buffalo, a powerful wild cattle species found in sub-Saharan savannas and forests.
-
B.
Syntrillium Software
Syntrillium Software was a software company best known for creating the audio editing program Cool Edit, which later evolved into Adobe Audition after Adobe acquired the firm.
-
C.
Taurus Systems GmbH
Taurus Systems GmbH is a German defense company specializing in the development and production of long-range precision-guided cruise missiles.
-
D.
SIGA Technologies
SIGA Technologies is a pharmaceutical company specializing in the development of antiviral treatments, particularly for smallpox and other orthopoxvirus infections.
-
E.
Argotec
Argotec is an Italian aerospace engineering company specializing in the design and development of small satellites and space systems for scientific and commercial missions.
- 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_69d889d3adc881909319f1edb8d2a956 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e439d18e3081908ca15baa743abcd8 |
completed | April 19, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a018c4a6630819082998cf754e8361f |
completed | May 11, 2026, 7:59 a.m. |
Created at: April 10, 2026, 5:43 a.m.