Triple
T27425648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rue de la Ferronnerie |
E690476
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Rue des Halles
Rue des Halles is a central Parisian street in the 1st arrondissement, historically linked to the former Les Halles market district and now part of a major commercial and transit hub.
|
E1791027
|
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: Rue des Halles | Statement: [Rue de la Ferronnerie, locatedNear, Rue des Halles]
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: Rue des Halles Triple: [Rue de la Ferronnerie, locatedNear, Rue des Halles]
Generated description
Rue des Halles is a central Parisian street in the 1st arrondissement, historically linked to the former Les Halles market district and now part of a major commercial and transit hub.
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_69ef52003fb48190b0f1295246182a86 |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f62d546b2881909d0acb99ce291de1 |
completed | May 2, 2026, 4:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12f700468c8190ad259becb0248d7e |
completed | May 24, 2026, 1:02 p.m. |
| NEDg | Description generation | batch_6a12f7ec5a388190912cedf024233dee |
completed | May 24, 2026, 1:06 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a12fb9bdbe881909c9f79d153f151a3 |
completed | May 24, 2026, 1:22 p.m. |
Created at: April 27, 2026, 12:40 p.m.