Triple
T37830276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Winter Springs, Florida |
E943179
|
entity |
| Predicate | hasElementarySchool |
P113
|
FINISHED |
| Object |
Layer Elementary School
Layer Elementary School is a public primary school serving students in the Winter Springs, Florida community.
|
E2245792
|
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: Layer Elementary School | Statement: [Winter Springs, Florida, hasElementarySchool, Layer Elementary 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: Layer Elementary School Triple: [Winter Springs, Florida, hasElementarySchool, Layer Elementary School]
Generated description
Layer Elementary School is a public primary school serving students in the Winter Springs, Florida community.
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_69f76eea4c8c8190a335aed5955cf2db |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbb1eded688190a77b8bcf9ed490b9 |
completed | May 6, 2026, 9:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a40fb8148a88190b1829900f305a3d7 |
completed | June 28, 2026, 10:46 a.m. |
| NEDg | Description generation | batch_6a40fe6fdd54819097f0b1029bba60be |
completed | June 28, 2026, 10:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a40fea1f42c8190928f74893d9a0ecb |
completed | June 28, 2026, 10:59 a.m. |
Created at: May 3, 2026, 4:19 p.m.