Triple
T26828994
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs. Arbuthnot |
E675444
|
entity |
| Predicate | relationshipToGeraldArbuthnot |
P198567
|
FINISHED |
| Object | mother |
—
|
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: mother | Statement: [Mrs. Arbuthnot, relationshipToGeraldArbuthnot, mother]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToGeraldArbuthnot Context triple: [Mrs. Arbuthnot, relationshipToGeraldArbuthnot, mother]
-
A.
relationshipToEarlJr
Indicates the specific familial or social relationship that an entity has to Earl Jr.
-
B.
relationshipToAlgernonMoncrieff
Indicates the specific familial, social, or personal connection that one entity has to Algernon Moncrieff.
-
C.
relationshipToHenry
Indicates the specific type of relationship or connection that an entity has to Henry.
-
D.
relationshipToHollyGolightly
Indicates the nature or type of relationship an entity has with Holly Golightly.
-
E.
relationshipTypeWithGwendolenFairfax
Indicates the specific nature or category of relationship that an entity has with Gwendolen Fairfax.
- 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_69eee9b776448190993a60b67fcc9545 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69fef3ceef648190b58027c93d757438 |
completed | May 9, 2026, 8:43 a.m. |
| PD | Predicate disambiguation | batch_69fef359da2c819091a034387b08821f |
completed | May 9, 2026, 8:42 a.m. |
| PDg | Predicate description generation | batch_69fef3cdea708190af41c1ff23b68f88 |
completed | May 9, 2026, 8:43 a.m. |
Created at: April 27, 2026, 5 a.m.