Triple
T2765217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snowdon |
E61320
|
entity |
| Predicate | listing |
P1278
|
FINISHED |
| Object | Furth |
E41600
|
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: Furth | Statement: [Snowdon, listing, Furth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Furth Context triple: [Snowdon, listing, Furth]
-
A.
Furth
chosen
A Furth is a mountain in the British Isles outside Scotland that meets the height and prominence criteria to be classified similarly to a Scottish Munro.
-
B.
Neu-Anspach
Neu-Anspach is a small town in the Hochtaunus district of Hesse, Germany, known for its proximity to the Taunus mountains and the open-air museum Hessenpark.
-
C.
Neustadt
Neustadt is a district of the Austrian city of Salzburg, known for its central urban character within the historic and cultural landscape of the city.
-
D.
Neustadt
Neustadt is a vibrant district of Dresden, Germany, known for its historic architecture, lively arts scene, and numerous bars, cafes, and cultural venues.
-
E.
Starnberg
Starnberg is a lakeside town in Bavaria, Germany, known for its affluent residential character and scenic location on Lake Starnberg southwest of Munich.
- 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_69ab4b7bab6c8190a5c2efef19a8ef34 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdd55a96c8190a109a1f0e8752477 |
completed | March 7, 2026, 8:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc048bc8481908a6f70e034167c2a |
completed | March 10, 2026, 6:55 a.m. |
Created at: March 6, 2026, 9:57 p.m.