Triple
T24319843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Avanti R4 |
E612928
|
entity |
| Predicate | relatedModel |
P37
|
FINISHED |
| Object |
Avanti R2
The Avanti R2 is an earlier model of the distinctive fiberglass-bodied Avanti sports coupe, known for its sleek styling and performance-oriented design.
|
E1630572
|
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: Avanti R2 | Statement: [Avanti R4, relatedModel, Avanti R2]
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: Avanti R2 Triple: [Avanti R4, relatedModel, Avanti R2]
Generated description
The Avanti R2 is an earlier model of the distinctive fiberglass-bodied Avanti sports coupe, known for its sleek styling and performance-oriented design.
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_69e2d7da491c8190b6e6218af50923db |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f292aa63fc8190a874367c9010f283 |
completed | April 29, 2026, 11:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0fd65779488190ae1e8e53fef449ea |
completed | May 22, 2026, 4:06 a.m. |
| NEDg | Description generation | batch_6a0fd79af7dc81909b36001ba18566fa |
completed | May 22, 2026, 4:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0fd86469288190aa03fe497754bad3 |
completed | May 22, 2026, 4:15 a.m. |
Created at: April 18, 2026, 1:48 a.m.