Triple
T26102342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clinton Greene |
E658442
|
entity |
| Predicate | relationshipToSheilaGreene |
P195625
|
FINISHED |
| Object | husband |
—
|
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: husband | Statement: [Clinton Greene, relationshipToSheilaGreene, husband]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToSheilaGreene Context triple: [Clinton Greene, relationshipToSheilaGreene, husband]
-
A.
relationshipToAgnes
Indicates the specific familial, social, or other relational connection that one entity has to the person named Agnes.
-
B.
relationshipToLaurie
Indicates the specific type of relationship or connection that an entity has to Laurie.
-
C.
relationshipToMary
Indicates that one entity stands in a specified personal or social relationship to Mary.
-
D.
relationshipToEva
Indicates a specified type of personal or social relationship that an entity has with Eva.
-
E.
relationshipToDianaGoodman
Indicates a specified type of relationship or connection that an entity has to Diana Goodman.
- 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_69ee5bc09c288190bc42a11972841383 |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69fddd373cdc8190be1b12e70e4deb1f |
completed | May 8, 2026, 12:55 p.m. |
| PD | Predicate disambiguation | batch_69fddc6915a88190ad41e379aa3ede13 |
completed | May 8, 2026, 12:51 p.m. |
| PDg | Predicate description generation | batch_69fddd364c1481908794c9d423bdc2d7 |
completed | May 8, 2026, 12:55 p.m. |
Created at: April 26, 2026, 7:55 p.m.