Triple
T3995277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Professor at Massachusetts Institute of Technology |
E87083
|
entity |
| Predicate | employerFocus |
P53735
|
FINISHED |
| Object | science |
—
|
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: science | Statement: [Professor at Massachusetts Institute of Technology, employerFocus, science]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employerFocus Context triple: [Professor at Massachusetts Institute of Technology, employerFocus, science]
-
A.
employerIn
Indicates that one entity serves as the employer of another within a specified context, such as a location, organization, or time period.
-
B.
employment
Indicates a relationship where one entity hires, contracts, or otherwise engages another to perform work or services, typically in exchange for compensation.
-
C.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
-
D.
peakEmployment
Indicates that an entity has reached its highest level of employment or workforce size during a specified period.
-
E.
businessCareer
Indicates a relationship where an entity’s professional life, roles, or progression is specifically within the field of business or commerce.
- F. None of above. chosen
Provenance (4 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_69aed94118148190975e6aa4e554cde9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb81040481909b22e4c445ecae0f |
completed | March 9, 2026, 4:55 p.m. |
| PD | Predicate disambiguation | batch_69aef8f692008190bf4d637ffc3d3eaa |
completed | March 9, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69aefb7f92348190ae35f1d75b0b5d4f |
completed | March 9, 2026, 4:55 p.m. |
Created at: March 9, 2026, 3:34 p.m.