Triple
T30198358
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abbey of Rouge-Cloître |
E767695
|
entity |
| Predicate | hasNameInFrench |
P6538
|
FINISHED |
| Object |
Abbaye du Rouge-Cloître
Abbaye du Rouge-Cloître is a former Augustinian priory and historic monastic site located in the Sonian Forest on the outskirts of Brussels, Belgium.
|
E1904379
|
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: Abbaye du Rouge-Cloître | Statement: [Abbey of Rouge-Cloître, hasNameInFrench, Abbaye du Rouge-Cloître]
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: Abbaye du Rouge-Cloître Triple: [Abbey of Rouge-Cloître, hasNameInFrench, Abbaye du Rouge-Cloître]
Generated description
Abbaye du Rouge-Cloître is a former Augustinian priory and historic monastic site located in the Sonian Forest on the outskirts of Brussels, Belgium.
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_69f2247db1108190835c0727c97637c3 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f67fc333508190b65ece66b1b573f7 |
completed | May 2, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a27589c4d888190bde41e1b0f42dacf |
completed | June 9, 2026, 12:04 a.m. |
| NEDg | Description generation | batch_6a275a7f3e7c8190bd79a2bad2e66ca1 |
completed | June 9, 2026, 12:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a275e9f9f148190ab1d1fd5ebc2d6bd |
completed | June 9, 2026, 12:30 a.m. |
Created at: April 29, 2026, 7:30 p.m.