Triple
T11666457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mame Dennis |
E277261
|
entity |
| Predicate | relationshipToPatrickDennis |
P100866
|
FINISHED |
| Object | aunt and guardian |
—
|
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: aunt and guardian | Statement: [Mame Dennis, relationshipToPatrickDennis, aunt and guardian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToPatrickDennis Context triple: [Mame Dennis, relationshipToPatrickDennis, aunt and guardian]
-
A.
relationshipToPatsey
Indicates the nature or type of relationship an entity has with the person or entity named Patsey.
-
B.
relationshipToPaul
Indicates a specified type of personal or social relationship that an entity has with Paul.
-
C.
relationshipToTony
Indicates the specific type of relationship or connection that an entity has with Tony.
-
D.
relationshipToDudley
Indicates the specific familial or social relationship that one entity has to the person named Dudley.
-
E.
relationshipToDani
Indicates the specific type of relationship or connection that an entity has to Dani.
- 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_69d6aafd0a448190b44da30af8c6c519 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a43f438081909da476294a057c38 |
completed | April 10, 2026, 7:18 a.m. |
| PD | Predicate disambiguation | batch_69d88a73f9ac819095662042804bf40a |
completed | April 10, 2026, 5:28 a.m. |
| PDg | Predicate description generation | batch_69d890458d948190b15054c9ba0fd923 |
completed | April 10, 2026, 5:53 a.m. |
Created at: April 8, 2026, 9:39 p.m.