Triple
T1867191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Beth Sully |
E34948
|
entity |
| Predicate | marriedBefore |
P33258
|
FINISHED |
| Object | Douglas Fairbanks' rise to Hollywood fame |
—
|
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: Douglas Fairbanks' rise to Hollywood fame | Statement: [Anna Beth Sully, marriedBefore, Douglas Fairbanks' rise to Hollywood fame]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedBefore Context triple: [Anna Beth Sully, marriedBefore, Douglas Fairbanks' rise to Hollywood fame]
-
A.
marriedAfter
Indicates that one marriage occurred later in time than another specified marriage.
-
B.
marriedInto
Indicates that one entity became connected to another’s family or group through marriage, rather than by birth or prior membership.
-
C.
neverMarried
Indicates that the subject has not been legally married to any partner at any time up to the present.
-
D.
numberOfMarriagesOfSpouse
Indicates the total count of times the referenced spouse has been married.
-
E.
marries
Indicates that one entity enters into a legally or socially recognized marital union with 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_69a88600b2f88190bc09303e68ab517e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abb16c09e48190a345c95eab59fd87 |
completed | March 7, 2026, 5:02 a.m. |
| PD | Predicate disambiguation | batch_69abafe02c3c819093a4744b476106ca |
completed | March 7, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69abb16a6db48190af04012e8ed2269f |
completed | March 7, 2026, 5:02 a.m. |
Created at: March 4, 2026, 7:34 p.m.