Triple
T29410977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tiberius |
E745892
|
entity |
| Predicate | marriageOrderWithJuliaTheElder |
P180437
|
FINISHED |
| Object | third husband of Julia the Elder |
—
|
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: third husband of Julia the Elder | Statement: [Tiberius, marriageOrderWithJuliaTheElder, third husband of Julia the Elder]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriageOrderWithJuliaTheElder Context triple: [Tiberius, marriageOrderWithJuliaTheElder, third husband of Julia the Elder]
-
A.
marriageOrderWithBelaLugosi
Indicates the chronological order in which an entity entered into marriage with Bela Lugosi.
-
B.
marriageOrderWithDorisDay
Indicates the ordinal position in which someone married Doris Day relative to her other spouses.
-
C.
marriageOrderToBelaLugosi
Indicates the sequence or ranking of a person’s marriage in relation to their marriage to Bela Lugosi.
-
D.
marriageOrderToVictorMature
Indicates that one entity is formally ordered or destined to marry Victor when he is mature or reaches adulthood.
-
E.
marriageOrderWithMayaAngelou
Indicates the ordinal position in which an entity married Maya Angelou relative to her other spouses.
- 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_69f0a79eb7d081908c67197a5f347e68 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69f74062b9388190b30546cf700a825c |
completed | May 3, 2026, 12:32 p.m. |
| PD | Predicate disambiguation | batch_69f73c802b848190b61a416b7488bd96 |
completed | May 3, 2026, 12:16 p.m. |
| PDg | Predicate description generation | batch_69f74061c440819080434155c2d60341 |
completed | May 3, 2026, 12:32 p.m. |
Created at: April 28, 2026, 2:57 p.m.