Triple
T30270802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Episcopal City of Fréjus |
E769787
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object |
historic center of Fréjus
The historic center of Fréjus is the old quarter of the Provençal town of Fréjus in southeastern France, known for its Roman and medieval heritage, narrow streets, and notable religious and civic monuments.
|
E1908861
|
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: historic center of Fréjus | Statement: [Episcopal City of Fréjus, partOf, historic center of Fréjus]
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: historic center of Fréjus Triple: [Episcopal City of Fréjus, partOf, historic center of Fréjus]
Generated description
The historic center of Fréjus is the old quarter of the Provençal town of Fréjus in southeastern France, known for its Roman and medieval heritage, narrow streets, and notable religious and civic monuments.
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_69f224856d9881908c7f0dd64f059672 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f680d43c708190a28635b6f09d3895 |
completed | May 2, 2026, 10:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a276ef65d3c81909481ad44da61757e |
completed | June 9, 2026, 1:40 a.m. |
| NEDg | Description generation | batch_6a276fd755b08190b6b6ef8d78b455aa |
completed | June 9, 2026, 1:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a2771970b548190ae6a535834242983 |
completed | June 9, 2026, 1:51 a.m. |
Created at: April 29, 2026, 7:43 p.m.