Triple
T27270628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eimeo |
E688040
|
entity |
| Predicate | hasEducationalInstitution |
P113
|
FINISHED |
| Object |
Eimeo Road State School
Eimeo Road State School is a primary school located in the coastal suburb of Eimeo in Queensland, Australia.
|
E1763241
|
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: Eimeo Road State School | Statement: [Eimeo, hasEducationalInstitution, Eimeo Road State School]
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: Eimeo Road State School Triple: [Eimeo, hasEducationalInstitution, Eimeo Road State School]
Generated description
Eimeo Road State School is a primary school located in the coastal suburb of Eimeo in Queensland, Australia.
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_69ef3558cf8881909595ef89daf6e14a |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69f62722eba88190b66a514b023a1197 |
completed | May 2, 2026, 4:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12628d73f88190aab621ae0474e375 |
completed | May 24, 2026, 2:29 a.m. |
| NEDg | Description generation | batch_6a126667dd3c819085340bab8ad5f43e |
completed | May 24, 2026, 2:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a1266dd3b748190a06a76a7587eff99 |
completed | May 24, 2026, 2:47 a.m. |
Created at: April 27, 2026, 10:59 a.m.