Triple
T3232625
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Livia Orestilla |
E67776
|
entity |
| Predicate | marriageToCaligulaDuration |
P45715
|
FINISHED |
| Object | very brief |
—
|
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: very brief | Statement: [Livia Orestilla, marriageToCaligulaDuration, very brief]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriageToCaligulaDuration Context triple: [Livia Orestilla, marriageToCaligulaDuration, very brief]
-
A.
endTime (marriage to Joséphine)
Indicates the point in time at which the marriage to Joséphine legally or formally ended.
-
B.
endTime (marriage to Donald Trump)
Indicates the date and time at which the marriage to Donald Trump legally or formally concluded.
-
C.
hasMarriagePlot
Indicates that the work’s narrative centrally involves courtship, romantic relationships, or the progression toward marriage as a key plot element.
-
D.
marriageDate
Indicates the specific date on which two entities entered into a marital relationship.
-
E.
numberOfMarriages
Indicates the total count of times an entity has been legally married.
- 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_69ad858c61888190a31196310d9b30b5 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaedb718c8190aae12f763033713a |
completed | March 8, 2026, 5:16 p.m. |
| PD | Predicate disambiguation | batch_69ad9e0dc2248190a38c40f4e06cd41c |
completed | March 8, 2026, 4:04 p.m. |
| PDg | Predicate description generation | batch_69ada0f9d5748190a405c89ec8f30264 |
completed | March 8, 2026, 4:16 p.m. |
Created at: March 8, 2026, 3:08 p.m.