Triple
T38110142
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramon Berenguer III |
E951631
|
entity |
| Predicate | titleAcquiredThroughMarriage |
P200831
|
FINISHED |
| Object | County of Provence |
—
|
NE NERFINISHED |
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: County of Provence | Statement: [Ramon Berenguer III, titleAcquiredThroughMarriage, County of Provence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: titleAcquiredThroughMarriage Context triple: [Ramon Berenguer III, titleAcquiredThroughMarriage, County of Provence]
-
A.
titleHolderByMarriage
Indicates that an individual holds a title not in their own right but by virtue of being married to the person who holds that title substantively.
-
B.
titleFormedByMarriage
chosen
Indicates that a person’s title or noble rank was acquired or changed as a result of marriage to another individual.
-
C.
titleHoldersMarriedInto
Indicates that the holders of a particular title became related to another party through marriage.
-
D.
titleFromSpouse
Indicates that an entity holds a title or honorific that is derived from or acquired through their spouse.
-
E.
titleAfterSecondMarriage
Indicates the title or designation a person holds specifically after entering into their second marriage.
- F. None of above.
Provenance (3 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_69f76f065ed08190bdfb1b6d817f5b39 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a0010e46d948190a51111b5270fade7 |
completed | May 10, 2026, 5 a.m. |
| PD | Predicate disambiguation | batch_6a001061d34c8190bfe73f3d7c061eb7 |
completed | May 10, 2026, 4:58 a.m. |
Created at: May 3, 2026, 4:21 p.m.