Triple
T27146602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Van Heemstra family |
E681964
|
entity |
| Predicate | hasFemaleTitle |
P1805
|
FINISHED |
| Object | baroness |
—
|
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: baroness | Statement: [Van Heemstra family, hasFemaleTitle, baroness]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFemaleTitle Context triple: [Van Heemstra family, hasFemaleTitle, baroness]
-
A.
hasFemaleTitleCharacter
Indicates that the subject work includes at least one female character whose title or role is explicitly referenced in its title.
-
B.
hasGenderedTitle
chosen
Indicates that an entity is associated with a title or form of address that is explicitly marked for a particular gender.
-
C.
officeHolderTitleWhenFemale
Indicates the specific title used for a person holding an office when that office holder is female.
-
D.
usedBothMaleAndFemaleTitles
Indicates that an entity has been referred to or addressed using both male and female honorifics or titles.
-
E.
hasWomenTagOrTriosLikeTitle
Indicates that the item’s title suggests it involves women or trio-related content, based on specific tags or title patterns.
- 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_69eefacca3888190b67238d380e8f28b |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f6691f5e188190b12c7b2eb729a45e |
completed | May 2, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69f66598d6008190a7ca8ff80399fd34 |
completed | May 2, 2026, 8:59 p.m. |
Created at: April 27, 2026, 9:11 a.m.