Triple
T19613306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laura Spelman Rockefeller Memorial |
E470788
|
entity |
| Predicate | namedForRelationship |
P136469
|
FINISHED |
| Object | wife of John D. Rockefeller Sr. |
—
|
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: wife of John D. Rockefeller Sr. | Statement: [Laura Spelman Rockefeller Memorial, namedForRelationship, wife of John D. Rockefeller Sr.]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: namedForRelationship Context triple: [Laura Spelman Rockefeller Memorial, namedForRelationship, wife of John D. Rockefeller Sr.]
-
A.
namedForField
Indicates that one entity is named after, or in honor of, a particular field, discipline, or area of study.
-
B.
namedForFamily
Indicates that one entity is named in honor of, or derived from the name of, a particular family or family group.
-
C.
namedForFeature
Indicates that one entity is named after, or derives its name from, a particular feature or characteristic of another entity.
-
D.
pairedWithRelationshipName
Indicates that one entity is associated with another as a named pair within a specific relationship context.
-
E.
namedAs
Indicates that one entity is given, known by, or referred to using the name of another entity.
- 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_69d8e510fa248190b7afb274a1d4cf73 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e640cd5de48190a9f7bab4da3f5b5a |
completed | April 20, 2026, 3:05 p.m. |
| PD | Predicate disambiguation | batch_69e514e166dc8190a0f147e0b4c8bbe7 |
completed | April 19, 2026, 5:46 p.m. |
| PDg | Predicate description generation | batch_69e5174b060c81908937ff9ff7fce611 |
completed | April 19, 2026, 5:56 p.m. |
Created at: April 10, 2026, 1:43 p.m.