Triple
T24517341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fanes-Sennes-Braies Nature Park |
E606409
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Sennes plateau
The Sennes plateau is a high-altitude karst plateau in the Dolomites of northern Italy, known for its expansive alpine meadows, limestone landscapes, and extensive hiking and cross-country skiing trails.
|
E1638160
|
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: Sennes plateau | Statement: [Fanes-Sennes-Braies Nature Park, contains, Sennes plateau]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Sennes plateau Triple: [Fanes-Sennes-Braies Nature Park, contains, Sennes plateau]
Generated description
The Sennes plateau is a high-altitude karst plateau in the Dolomites of northern Italy, known for its expansive alpine meadows, limestone landscapes, and extensive hiking and cross-country skiing trails.
Provenance (5 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_69e2c4c725148190a4e41577c5cb409c |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2a852223081908f3b99a409316f6b |
completed | April 30, 2026, 12:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0fee93682881908d2e820e27e33e94 |
completed | May 22, 2026, 5:50 a.m. |
| NEDg | Description generation | batch_6a0fef865e8c81909c338c647f756c65 |
completed | May 22, 2026, 5:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ff097dd8881908bb83d84a6581ef7 |
completed | May 22, 2026, 5:58 a.m. |
Created at: April 18, 2026, 2:24 a.m.