Triple
T27703531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Villers Abbey |
E698489
|
entity |
| Predicate | hasNameInDutch |
P13254
|
FINISHED |
| Object |
Abdij van Villers
Abdij van Villers is a ruined Cistercian monastery in Villers-la-Ville, Belgium, renowned for its impressive medieval architecture and atmospheric remains.
|
E1786191
|
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: Abdij van Villers | Statement: [Villers Abbey, hasNameInDutch, Abdij van Villers]
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: Abdij van Villers Triple: [Villers Abbey, hasNameInDutch, Abdij van Villers]
Generated description
Abdij van Villers is a ruined Cistercian monastery in Villers-la-Ville, Belgium, renowned for its impressive medieval architecture and atmospheric remains.
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_69ef590ea74081908f0cd7500d85fa27 |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69f635a522248190a3c43a4a6aa65e1f |
completed | May 2, 2026, 5:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12e45d4f80819084d10e56eede036c |
completed | May 24, 2026, 11:43 a.m. |
| NEDg | Description generation | batch_6a12e4fca4088190b20187243cbf974e |
completed | May 24, 2026, 11:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a12e606bc688190958b5e84777566fb |
completed | May 24, 2026, 11:50 a.m. |
Created at: April 27, 2026, 2:58 p.m.