Triple
T31638083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Davis Institute for Medical Research |
E807367
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Lady Henrietta Davis
Lady Henrietta Davis was a benefactor whose support and legacy in health and science are commemorated through the Lady Davis Institute for Medical Research.
|
E1971379
|
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: Lady Henrietta Davis | Statement: [Lady Davis Institute for Medical Research, namedAfter, Lady Henrietta Davis]
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: Lady Henrietta Davis Triple: [Lady Davis Institute for Medical Research, namedAfter, Lady Henrietta Davis]
Generated description
Lady Henrietta Davis was a benefactor whose support and legacy in health and science are commemorated through the Lady Davis Institute for Medical Research.
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_69f348d892948190915f8facacb9568c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6a9188ad88190a84a1aac280d3d3c |
completed | May 3, 2026, 1:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2b79daba188190b3666e4e4f1d2fb5 |
completed | June 12, 2026, 3:15 a.m. |
| NEDg | Description generation | batch_6a2b7c00f3f481908374741f61c6e10c |
completed | June 12, 2026, 3:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a2b7cc8af648190bc6c0b9472a89846 |
completed | June 12, 2026, 3:28 a.m. |
Created at: April 30, 2026, 10:48 p.m.