Triple
T3203954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gagan Biyani |
E67115
|
entity |
| Predicate | hasSectorExperience |
P17879
|
FINISHED |
| Object | education technology sector |
—
|
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: education technology sector | Statement: [Gagan Biyani, hasSectorExperience, education technology sector]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSectorExperience Context triple: [Gagan Biyani, hasSectorExperience, education technology sector]
-
A.
typeOfExperience
Indicates that one entity specifies the category or nature of an experience associated with another entity.
-
B.
experienceType
Indicates the specific kind or category of experience associated with an entity or event.
-
C.
hasWorkedIn
chosen
Indicates that a person has been employed or has performed work within a particular organization, location, or domain for some period of time.
-
D.
isSectorSpecific
Indicates that something is tailored or restricted to a particular industry or sector rather than being generally applicable.
-
E.
softwareExperience
Indicates the level or extent of a person's prior experience working with or using specific software.
- 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_69ad8589bd988190afa7ed2bdffb7b33 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaa54124c8190a22089ce2eaedab5 |
completed | March 8, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69ad9e078f7c8190813d9fcb4f5071fb |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:07 p.m.