Triple
T24924661
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Countess of Burlington |
E618816
|
entity |
| Predicate | hasMaleEquivalentTitle |
P15994
|
FINISHED |
| Object | Earl of Burlington |
—
|
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: Earl of Burlington | Statement: [Countess of Burlington, hasMaleEquivalentTitle, Earl of Burlington]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMaleEquivalentTitle Context triple: [Countess of Burlington, hasMaleEquivalentTitle, Earl of Burlington]
-
A.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
-
B.
hasGenderedTitle
Indicates that an entity is associated with a title or form of address that is explicitly marked for a particular gender.
-
C.
maleEquivalent
chosen
Indicates that one entity is the corresponding male counterpart or equivalent of another entity.
-
D.
oppositeTitleByGender
Indicates that one title is the gender-based counterpart of another title (e.g., king/queen, actor/actress).
-
E.
usedBothMaleAndFemaleTitles
Indicates that an entity has been referred to or addressed using both male and female honorifics or titles.
- 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_69e2fab9edd88190b86004a78a28bc20 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f65f7731e4819099d5bd3d915ee266 |
completed | May 2, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f65c1f94ac8190bc6fbc7916fc0d82 |
completed | May 2, 2026, 8:18 p.m. |
Created at: April 18, 2026, 5:29 a.m.