Triple
T30832681
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | R.F. Simpson |
E785265
|
entity |
| Predicate | relationshipToLinaLamont |
P204266
|
FINISHED |
| Object | employer |
—
|
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: employer | Statement: [R.F. Simpson, relationshipToLinaLamont, employer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToLinaLamont Context triple: [R.F. Simpson, relationshipToLinaLamont, employer]
-
A.
relationshipToNina
Indicates that one entity has a specified personal or social relationship to Nina.
-
B.
relationshipToLavinia
Indicates the nature or type of relationship an entity has with Lavinia.
-
C.
relationshipTypeWithSamMalone
Indicates the specific nature or category of relationship that an entity has with Sam Malone.
-
D.
relationshipToTinaBordereau
Indicates the specific type of personal or professional relationship an entity has with Tina Bordereau.
-
E.
relationshipTypeWithLorraineBroughton
Indicates the specific nature or category of relationship an entity has with Lorraine Broughton.
- 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_69f224b73d8c81908129383bfb397c87 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a035b78140081908e451cb63d3c1c7c |
completed | May 12, 2026, 4:55 p.m. |
| PD | Predicate disambiguation | batch_6a035af3d8f48190bf1f3741d374404e |
completed | May 12, 2026, 4:53 p.m. |
| PDg | Predicate description generation | batch_6a035b773898819099125a5efc239d3e |
completed | May 12, 2026, 4:55 p.m. |
Created at: April 29, 2026, 8:44 p.m.