Triple
T19843810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Countess of Survilliers |
E476804
|
entity |
| Predicate | formerTitleHolderRole |
P59714
|
FINISHED |
| Object | Queen consort of Naples |
—
|
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: Queen consort of Naples | Statement: [Countess of Survilliers, formerTitleHolderRole, Queen consort of Naples]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: formerTitleHolderRole Context triple: [Countess of Survilliers, formerTitleHolderRole, Queen consort of Naples]
-
A.
predecessorTitleHolder
Indicates that one entity previously held a particular title or position before another entity.
-
B.
formerMemberName
Indicates that the referenced name belongs to an entity that was previously a member of a particular group, organization, or body but is no longer one.
-
C.
lastHolderTitle
Indicates the official title or position held by the most recent holder of a given role, asset, or entity.
-
D.
notableFormerHolderRole
chosen
Indicates that an entity previously held a particular notable role or position.
-
E.
heldTitleFrom
Indicates that an entity possessed or held a particular title starting from a specified point in time.
- 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_69d8e51d39d081909bcfafeaaf3d2fcc |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e658085a148190a305bde0897dfe84 |
completed | April 20, 2026, 4:44 p.m. |
| PD | Predicate disambiguation | batch_69e537e21d2881909b1be82f02b99d40 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:51 p.m.