Triple
T23126407
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tasman Lake |
E577043
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Hooker Lake
Hooker Lake is a glacial lake in New Zealand’s Aoraki/Mount Cook National Park, popular for its striking icebergs and alpine scenery beneath Aoraki/Mount Cook.
|
E2293959
|
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: Hooker Lake | Statement: [Tasman Lake, locatedNear, Hooker Lake]
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: Hooker Lake Triple: [Tasman Lake, locatedNear, Hooker Lake]
Generated description
Hooker Lake is a glacial lake in New Zealand’s Aoraki/Mount Cook National Park, popular for its striking icebergs and alpine scenery beneath Aoraki/Mount Cook.
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_69e245f7b0e481909c473ff4e6a54e2c |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e5482588190b95b36075ecc7f24 |
completed | April 29, 2026, 4:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a7b5b37d28481909d223d2dbcbf797a |
completed | Aug. 11, 2026, 5:26 p.m. |
| NEDg | Description generation | batch_6a7b5b7e4b688190a44b42531702c33c |
completed | Aug. 11, 2026, 5:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a7b5bc39a988190afc763e4f495b9fa |
completed | Aug. 11, 2026, 5:28 p.m. |
Created at: April 17, 2026, 3:59 p.m.