Triple
T30912935
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dorothy Davenport |
E787504
|
entity |
| Predicate | usedProfessionalNameAfterSpouseDeath |
P172194
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Dorothy Davenport, usedProfessionalNameAfterSpouseDeath, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedProfessionalNameAfterSpouseDeath Context triple: [Dorothy Davenport, usedProfessionalNameAfterSpouseDeath, true]
-
A.
namedForSpouse
Indicates that one entity is named after the spouse of another entity.
-
B.
laterMarriedName
Indicates that the referenced name is a surname or full name a person adopted after a later marriage, replacing or succeeding their previous name.
-
C.
spouseNameAtDeath
Indicates the name of a person's spouse at the time of that person's death.
-
D.
spouse name
Indicates that one entity is the legally recognized husband or wife of the other, specifying the partner’s name in a marital relationship.
-
E.
spouseNameAtMarriage
Indicates the full name a person’s spouse had at the time of their marriage.
- 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_69f224be300c8190a6513ce1ee0a7026 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6a9603b208190b3533ea2b441514c |
completed | May 3, 2026, 1:48 a.m. |
| PD | Predicate disambiguation | batch_69f6a7548eb48190a69b60a3c6ad53b9 |
completed | May 3, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f6a915ead881909463ae46419c343e |
completed | May 3, 2026, 1:47 a.m. |
Created at: April 29, 2026, 8:51 p.m.