Triple
T4133243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steppe Road |
E85088
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Eurasia |
E9404
|
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: Eurasia | Statement: [Steppe Road, locatedIn, Eurasia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eurasia Context triple: [Steppe Road, locatedIn, Eurasia]
-
A.
Eurasia
chosen
Eurasia is the vast combined continental landmass of Europe and Asia, forming the largest continuous land area on Earth.
-
B.
Afro-Eurasia
Afro-Eurasia is the vast continuous landmass comprising the continents of Africa, Europe, and Asia, forming the largest connected continental area on Earth.
-
C.
Europa
Europa is a figure in Greek mythology, a Phoenician princess famously abducted by Zeus and later the eponymous queen of Crete.
-
D.
Europa
Europa is one of Jupiter’s large icy moons, notable for its smooth frozen surface and the subsurface ocean that makes it a prime candidate in the search for extraterrestrial life.
-
E.
Europa
Europa is a European-themed section of the Worlds of Fun amusement park in Kansas City, Missouri, featuring attractions, architecture, and cuisine inspired by various European countries.
- 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_69aed935ccd881909dc61f81bcdb7a78 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af02306d608190bf8945d560f87c21 |
completed | March 9, 2026, 5:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b576c4c0888190be156d093ea11207 |
completed | March 14, 2026, 2:55 p.m. |
Created at: March 9, 2026, 3:43 p.m.