Triple
T28137012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Savoy Palace |
E714235
|
entity |
| Predicate | laterUseOfSite |
P6937
|
FINISHED |
| Object |
Savoy Hospital
Savoy Hospital was a prominent medieval charitable institution in London that provided care for the poor and sick on the former site of the Savoy Palace.
|
E1806339
|
NE FINISHED |
How this triple was built (3 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: Savoy Hospital | Statement: [Savoy Palace, laterUseOfSite, Savoy Hospital]
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: Savoy Hospital Triple: [Savoy Palace, laterUseOfSite, Savoy Hospital]
Generated description
Savoy Hospital was a prominent medieval charitable institution in London that provided care for the poor and sick on the former site of the Savoy Palace.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laterUseOfSite Context triple: [Savoy Palace, laterUseOfSite, Savoy Hospital]
-
A.
laterUseLocation
Indicates that an entity is located at a place where it will be used or utilized at a later time.
-
B.
subsequentUse
Indicates that one entity is used, applied, or consumed after another entity in time or sequence.
-
C.
laterUsedBy
chosen
Indicates that something is subsequently utilized or employed by a specified entity at a later time.
-
D.
mainSiteAfterUse
Indicates that one site or location becomes the primary or main site after a particular use, event, or operation has occurred.
-
E.
originalSiteUse
Indicates the historical or initial functional use or purpose assigned to a particular site before any subsequent changes or developments.
- F. None of above.
Provenance (6 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_69efd6af156c81908f50c2cd7db0e1ef |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69fd509e6bc08190b263923c2f40fea3 |
completed | May 8, 2026, 2:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a15d7a0277481908cd8f2f1b93059cf |
completed | May 26, 2026, 5:25 p.m. |
| NEDg | Description generation | batch_6a15da65e3f481909bcb009caacb671f |
completed | May 26, 2026, 5:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a15dda803a88190acf72fda12446639 |
completed | May 26, 2026, 5:51 p.m. |
| PD | Predicate disambiguation | batch_69fd4fd1a58881909d4b84de1b24e380 |
completed | May 8, 2026, 2:52 a.m. |
Created at: April 27, 2026, 9:50 p.m.