Triple
T2419875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marquess of Lansdowne |
E53392
|
entity |
| Predicate | titleHolderGender |
P28721
|
FINISHED |
| Object | usually male |
—
|
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: usually male | Statement: [Marquess of Lansdowne, titleHolderGender, usually male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: titleHolderGender Context triple: [Marquess of Lansdowne, titleHolderGender, usually male]
-
A.
titleHolderName
Indicates the name of the person or entity that holds a particular title or position.
-
B.
titleHolderType
Indicates the specific role or capacity in which an entity holds a title (e.g., owner, trustee, beneficiary).
-
C.
titleHolderMother
Indicates that the subject is the mother of the current or specified holder of a particular title.
-
D.
titleHolderIsUsually
chosen
Indicates that an entity serving as a title holder is typically or most commonly a particular type or category of entity.
-
E.
titleHolderFullName
Indicates the full personal name of the entity that holds a particular title or position.
- 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_69ab495c44d48190b7235b23719bc3f6 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abc9f342e88190a430b02842ded418 |
completed | March 7, 2026, 6:47 a.m. |
| PD | Predicate disambiguation | batch_69abc5a889948190b77de4ef6ac815a8 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 6, 2026, 9:42 p.m.