Triple
T28376601
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buena Vista Street |
E718771
|
entity |
| Predicate | hasSubArea |
P747
|
FINISHED |
| Object |
Los Feliz Five & Dime
Los Feliz Five & Dime is a vintage-themed retail shop in Disney California Adventure that sells apparel, accessories, and souvenirs inspired by classic Hollywood and early 20th-century Los Angeles.
|
E1813954
|
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: Los Feliz Five & Dime | Statement: [Buena Vista Street, hasSubArea, Los Feliz Five & Dime]
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: Los Feliz Five & Dime Triple: [Buena Vista Street, hasSubArea, Los Feliz Five & Dime]
Generated description
Los Feliz Five & Dime is a vintage-themed retail shop in Disney California Adventure that sells apparel, accessories, and souvenirs inspired by classic Hollywood and early 20th-century Los Angeles.
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_69eff6ee5afc8190bd7375a29f0cc6c6 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f64c5eee988190aa18cd909ea3e855 |
completed | May 2, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a1627d998848190bb75626a3c441dba |
completed | May 26, 2026, 11:08 p.m. |
| NEDg | Description generation | batch_6a1629cc5c108190a1c0d533c8845925 |
completed | May 26, 2026, 11:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a162a528ff8819090b6069a04b7758f |
completed | May 26, 2026, 11:18 p.m. |
Created at: April 28, 2026, 1:03 a.m.