Triple
T3003267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richard Burton |
E81838
|
entity |
| Predicate | marriageCountWithElizabethTaylor |
P44595
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Richard Burton, marriageCountWithElizabethTaylor, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriageCountWithElizabethTaylor Context triple: [Richard Burton, marriageCountWithElizabethTaylor, 2]
-
A.
numberOfMarriagesOfSpouse
Indicates the total count of times the referenced spouse has been married.
-
B.
numberOfMarriages
Indicates the total count of times an entity has been legally married.
-
C.
marriedToNotablePerson
Indicates that a person is legally married to another individual who is widely recognized or notable.
-
D.
marriageToCharlieChaplinEnd
Indicates the point or event at which a marriage to Charlie Chaplin comes to an end (e.g., through divorce, annulment, or death).
-
E.
marriageStartWithLaurenceOlivier
Indicates the point in time when a marriage involving Laurence Olivier began.
- 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_69ad8b1c4de88190a83b7cefaa1f2842 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a1371c481909e214234afed1a65 |
completed | March 8, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69ad96180eb08190a524c5f458d41382 |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad97f6af3881909f4547967384114c |
completed | March 8, 2026, 3:38 p.m. |
Created at: March 8, 2026, 2:59 p.m.