Triple
T24356731
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scarba |
E613941
|
entity |
| Predicate | hasNearbyWaterBody |
P1489
|
FINISHED |
| Object |
Sound of Luing
The Sound of Luing is a narrow sea channel off the west coast of Scotland, lying between the islands of Luing and Scarba in the Inner Hebrides.
|
E1630697
|
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: Sound of Luing | Statement: [Scarba, hasNearbyWaterBody, Sound of Luing]
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: Sound of Luing Triple: [Scarba, hasNearbyWaterBody, Sound of Luing]
Generated description
The Sound of Luing is a narrow sea channel off the west coast of Scotland, lying between the islands of Luing and Scarba in the Inner Hebrides.
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_69e2d7dfe7f08190b7a1f3a36483ab05 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f29349ee188190a9d54b2c725b8a27 |
completed | April 29, 2026, 11:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0fd66bcea88190ad739c45172c8eea |
completed | May 22, 2026, 4:07 a.m. |
| NEDg | Description generation | batch_6a0fd79af7dc81909b36001ba18566fa |
completed | May 22, 2026, 4:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0fd86469288190aa03fe497754bad3 |
completed | May 22, 2026, 4:15 a.m. |
Created at: April 18, 2026, 2 a.m.