Triple
T38393272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs. Van Buren |
E899783
|
entity |
| Predicate | relationshipTypeWithEsther |
P204637
|
FINISHED |
| Object | client |
—
|
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: client | Statement: [Mrs. Van Buren, relationshipTypeWithEsther, client]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithEsther Context triple: [Mrs. Van Buren, relationshipTypeWithEsther, client]
-
A.
relationshipTypeWithHesterCollyer
Indicates the specific nature or category of relationship that an entity has with Hester Collyer.
-
B.
relationshipToRichardElster
Indicates the specific type of personal or social relationship an entity has to Richard Elster.
-
C.
relationshipToEva
Indicates a specified type of personal or social relationship that an entity has with Eva.
-
D.
relationshipTypeWithVashti
Indicates the specific nature or category of the relationship an entity has with Vashti.
-
E.
relationshipToBeth
Indicates the specific type of relationship or connection that an entity has to Beth.
- 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_69f76e5c9b808190b486523f5c2f817d |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a1c850c819088795a7ae59bdeb8 |
completed | May 12, 2026, 7:06 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:31 p.m.