Triple
T5392941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Higgins Professor of Physics at Harvard University |
E120377
|
entity |
| Predicate | hasHolderType |
P38152
|
FINISHED |
| Object | faculty member |
—
|
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: faculty member | Statement: [Higgins Professor of Physics at Harvard University, hasHolderType, faculty member]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHolderType Context triple: [Higgins Professor of Physics at Harvard University, hasHolderType, faculty member]
-
A.
hasPositionHolderType
chosen
Indicates that an entity’s role or office is associated with a specific type or category of position holder.
-
B.
hasCurrentHolder
Indicates that a specified entity is the present holder, possessor, or occupant of another entity (such as a position, title, asset, or object).
-
C.
hasFirstHolder
Indicates that an entity is associated with the earliest or original holder (e.g., owner, position-bearer, or title-holder) of something.
-
D.
haveType
Indicates that an entity belongs to or is classified under a specified type or category.
-
E.
hasPreviousHolder
Indicates that an entity was formerly held, occupied, or possessed by another specified entity before the current one.
- 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_69bd46354c648190a38b26f107010a96 |
completed | March 20, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69bd871b81d08190993928e2c6251226 |
completed | March 20, 2026, 5:42 p.m. |
| PD | Predicate disambiguation | batch_69bd8463a9c88190bd760378f3026180 |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:04 p.m.