Triple
T29254394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Makalu massif |
E741656
|
entity |
| Predicate | hasGlacier |
P4580
|
FINISHED |
| Object |
Kangchung Glacier
Kangchung Glacier is a high-altitude Himalayan glacier situated on the slopes of the Makalu massif in eastern Nepal.
|
E1860620
|
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: Kangchung Glacier | Statement: [Makalu massif, hasGlacier, Kangchung Glacier]
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: Kangchung Glacier Triple: [Makalu massif, hasGlacier, Kangchung Glacier]
Generated description
Kangchung Glacier is a high-altitude Himalayan glacier situated on the slopes of the Makalu massif in eastern Nepal.
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_69f0911eba2c8190b07cd2fdf91422c9 |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69f664ac0f488190ba511fe5c68babfc |
completed | May 2, 2026, 8:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a25a85052b48190abcc50705f80f9ca |
completed | June 7, 2026, 5:20 p.m. |
| NEDg | Description generation | batch_6a25ad48a5e881908ec91dcd6da78001 |
completed | June 7, 2026, 5:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a25ada3d5888190b35903316b8b3e03 |
completed | June 7, 2026, 5:43 p.m. |
Created at: April 28, 2026, 12:36 p.m.