Triple
T16569865
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duc de Magenta |
E402555
|
entity |
| Predicate | titleHolderRankAttainedYear |
P18619
|
FINISHED |
| Object | Marshal of France 1859 |
—
|
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: Marshal of France 1859 | Statement: [Duc de Magenta, titleHolderRankAttainedYear, Marshal of France 1859]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: titleHolderRankAttainedYear Context triple: [Duc de Magenta, titleHolderRankAttainedYear, Marshal of France 1859]
-
A.
titleHolderRank
chosen
Indicates the relative position or level of precedence assigned to a title holder within a ranked order or hierarchy.
-
B.
titleHolderSince
Indicates the entity that has held a particular title or position continuously from a specified starting time.
-
C.
lastHolderRank
Indicates the rank or position held by the most recent entity that possessed or held a given item, title, or role.
-
D.
earliestKnownHolder
Indicates that the subject is the first known entity in time to have possessed, held, or been assigned the object.
-
E.
recordHolderMostTitles
Indicates that one entity holds the highest number of titles (e.g., championships, awards, or similar distinctions) compared to all other entities in a given context.
- 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_69d8838648088190acf97ef11fc3f61b |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e359580f548190a21afd148bc22d8d |
completed | April 18, 2026, 10:13 a.m. |
| PD | Predicate disambiguation | batch_69e296a47b7481909d9958158510c806 |
completed | April 17, 2026, 8:23 p.m. |
Created at: April 10, 2026, 5:16 a.m.