Triple
T21402531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Master of Industrial and Labor Relations |
E527945
|
entity |
| Predicate | mayLeadToCareerAs |
P90207
|
FINISHED |
| Object | human resources manager |
—
|
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: human resources manager | Statement: [Master of Industrial and Labor Relations, mayLeadToCareerAs, human resources manager]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mayLeadToCareerAs Context triple: [Master of Industrial and Labor Relations, mayLeadToCareerAs, human resources manager]
-
A.
targetCareer
Indicates that one entity is the intended or pursued career or professional goal of another entity.
-
B.
careerField
Indicates the professional domain or occupational area in which an entity works or specializes.
-
C.
careerType
Indicates the kind or category of professional occupation or career path associated with an entity.
-
D.
describesCareerOf
Indicates that one entity provides a description or characterization of the professional career of another entity.
-
E.
helpsLaunchCareerOf
chosen
Indicates that one entity plays a significant role in starting, advancing, or establishing the professional career of another entity.
- 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_69e0b520ee3c8190abddbee7e37e834c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b171336c819083ae5e2c0f5d4b95 |
completed | April 22, 2026, 11:30 a.m. |
| PD | Predicate disambiguation | batch_69e61633f8208190a2a849457c4e4198 |
completed | April 20, 2026, 12:04 p.m. |
Created at: April 16, 2026, 5:24 p.m.