Triple
T32532402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alice Keppel |
E831489
|
entity |
| Predicate | relationshipToQueenCamilla |
P206302
|
FINISHED |
| Object | great-grandmother |
—
|
LITERAL 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: great-grandmother | Statement: [Alice Keppel, relationshipToQueenCamilla, great-grandmother]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToQueenCamilla Context triple: [Alice Keppel, relationshipToQueenCamilla, great-grandmother]
-
A.
relationshipToAnneBoleyn
Indicates the specific familial, marital, or social connection that an entity has to Anne Boleyn.
-
B.
relationshipToCatherine
Indicates the specific familial, social, or interpersonal connection that one entity has to the person named Catherine.
-
C.
relationshipToDavidCameron
Indicates the specific type of personal, professional, or social relationship that one entity has to David Cameron.
-
D.
relationshipToPrincess
Indicates the specific familial, social, or romantic connection that one entity has to a princess.
-
E.
relationshipToQueenOfHearts
Indicates the specific familial, social, or hierarchical relationship that one entity has to the Queen of Hearts.
- F. None of above. chosen
Provenance (4 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_69f34924b1cc8190ad3aca0c0f012a7e |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379edf2d88190b492fca86ed23cac |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7fb9f88190b384b1b68200aef0 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:01 a.m.