Triple
T23823234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Le Conte Canyon |
E589292
|
entity |
| Predicate | containsFeature |
P182
|
FINISHED |
| Object |
Little Pete Meadow
Little Pete Meadow is a scenic alpine meadow in California’s Sierra Nevada, known for its lush grasses, surrounding granite peaks, and popularity with backpackers along the John Muir Trail.
|
E1602970
|
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: Little Pete Meadow | Statement: [Le Conte Canyon, containsFeature, Little Pete Meadow]
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: Little Pete Meadow Triple: [Le Conte Canyon, containsFeature, Little Pete Meadow]
Generated description
Little Pete Meadow is a scenic alpine meadow in California’s Sierra Nevada, known for its lush grasses, surrounding granite peaks, and popularity with backpackers along the John Muir Trail.
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_69e25d18619081909c7fb89d8926f14a |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1c7b12520819086daf8c284732e0f |
completed | April 29, 2026, 8:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0f699ab3548190ab4928e97fabe104 |
completed | May 21, 2026, 8:22 p.m. |
| NEDg | Description generation | batch_6a0f6a2b96048190b3f1e6465232f4ba |
completed | May 21, 2026, 8:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0f6d4eddf0819081caec7518121664 |
completed | May 21, 2026, 8:38 p.m. |
Created at: April 17, 2026, 7:59 p.m.