Triple
T27467692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | West Austin area |
E693218
|
entity |
| Predicate | hasNeighborhood |
P40
|
FINISHED |
| Object |
Rollingwood
Rollingwood is a small, affluent residential city just west of downtown Austin, Texas, known for its leafy streets, proximity to Zilker Park and Lady Bird Lake, and highly rated schools.
|
E1774131
|
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: Rollingwood | Statement: [West Austin area, hasNeighborhood, Rollingwood]
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: Rollingwood Triple: [West Austin area, hasNeighborhood, Rollingwood]
Generated description
Rollingwood is a small, affluent residential city just west of downtown Austin, Texas, known for its leafy streets, proximity to Zilker Park and Lady Bird Lake, and highly rated schools.
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_69ef538105548190a771cc5a0cf8c211 |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69f62dfe6f7881909a45a89d1b753b1a |
completed | May 2, 2026, 5:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12bbe0d6a881909af7363d313dc2c5 |
completed | May 24, 2026, 8:50 a.m. |
| NEDg | Description generation | batch_6a12bc482bb481908d121283f113dbb8 |
completed | May 24, 2026, 8:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a12bcd0c164819098f637afcad01642 |
completed | May 24, 2026, 8:54 a.m. |
Created at: April 27, 2026, 12:52 p.m.