Triple
T14532826
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sheriff Ed Earl Dodd |
E340956
|
entity |
| Predicate | relationshipTypeWith Mona Stangley |
P114654
|
FINISHED |
| Object | romantic relationship |
—
|
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: romantic relationship | Statement: [Sheriff Ed Earl Dodd, relationshipTypeWith Mona Stangley, romantic relationship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWith Mona Stangley Context triple: [Sheriff Ed Earl Dodd, relationshipTypeWith Mona Stangley, romantic relationship]
-
A.
relationshipToStanley
Indicates the specific type of personal or social relationship an entity has with Stanley.
-
B.
relationshipTypeWithStaceyColbert
Indicates the specific nature or category of relationship that an entity has with Stacey Colbert.
-
C.
relationshipTypeWith Alicia Johns
Indicates the specific type or nature of the relationship that an entity has with Alicia Johns.
-
D.
relationshipTypeWith Francesca Johnson
Indicates the specific nature or category of the relationship that an entity has with Francesca Johnson.
-
E.
relationshipDynamicWithStaceyColbert
Indicates a changing or evolving interpersonal relationship involving Stacey Colbert.
- 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_69d822dac79c8190a84a073f3cbaced5 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dea053f9bc8190901b9d321811d881 |
completed | April 14, 2026, 8:15 p.m. |
| PD | Predicate disambiguation | batch_69de5c518fc08190a6ce4d8be05c4c5d |
completed | April 14, 2026, 3:25 p.m. |
| PDg | Predicate description generation | batch_69de5fb5ac548190932f238e37271741 |
completed | April 14, 2026, 3:39 p.m. |
Created at: April 10, 2026, 1:22 a.m.