Triple
T35796095
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dadeland South station |
E1034834
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object |
Kendall Drive
Kendall Drive is a major east–west thoroughfare in Miami-Dade County, Florida, serving as a key commercial and commuter corridor through the Kendall area.
|
E2297015
|
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: Kendall Drive | Statement: [Dadeland South station, near, Kendall Drive]
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: Kendall Drive Triple: [Dadeland South station, near, Kendall Drive]
Generated description
Kendall Drive is a major east–west thoroughfare in Miami-Dade County, Florida, serving as a key commercial and commuter corridor through the Kendall area.
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_69f76e169bd081909f16cd8c9ee7870c |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a25541c081909a2c3780af82c516 |
completed | May 3, 2026, 7:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a82f46818f08190bcbce3c45be4422b |
completed | Aug. 17, 2026, 11:45 a.m. |
| NEDg | Description generation | batch_6a82f4e6f8a88190b76d9ae821715261 |
completed | Aug. 17, 2026, 11:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a82f53d16b88190bf3f9aa99b23039e |
completed | Aug. 17, 2026, 11:49 a.m. |
Created at: May 3, 2026, 4:06 p.m.