Triple
T9456776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mallinckrodt Professor of Physics and of Applied Physics at Harvard University |
E228033
|
entity |
| Predicate | holderGender |
P39348
|
FINISHED |
| Object | female |
—
|
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: female | Statement: [Mallinckrodt Professor of Physics and of Applied Physics at Harvard University, holderGender, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: holderGender Context triple: [Mallinckrodt Professor of Physics and of Applied Physics at Harvard University, holderGender, female]
-
A.
hasGenderOfPerson
chosen
Indicates that a person is associated with a specific gender classification.
-
B.
genderOfTypicalHolder
Indicates the gender that is most commonly associated with or typical of the usual holder of something.
-
C.
genderOfFirstHolder
Indicates that the relationship specifies the gender of the first entity that holds or possesses something in the described context.
-
D.
genderRule
Indicates a rule or constraint that determines how gender-related properties or classifications should be assigned or interpreted in a given context.
-
E.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or 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_69ca843b123881909b0e60028475d12d |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7f90cf648190ab238ba6b4f03c4f |
completed | April 1, 2026, 8:26 p.m. |
| PD | Predicate disambiguation | batch_69cca5596ffc819097e9c8eefd4ef9b8 |
completed | April 1, 2026, 4:55 a.m. |
Created at: March 30, 2026, 7:52 p.m.