Triple
T7676083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zanon ceramics factory |
E173863
|
entity |
| Predicate | academicInterest |
P74482
|
FINISHED |
| Object | case study in labor studies |
—
|
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: case study in labor studies | Statement: [Zanon ceramics factory, academicInterest, case study in labor studies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: academicInterest Context triple: [Zanon ceramics factory, academicInterest, case study in labor studies]
-
A.
subjectInterest
Indicates that the subject has an interest in, or is concerned with, the object.
-
B.
academicFocus
Indicates the primary field of study, discipline, or subject area that an entity concentrates on academically.
-
C.
regionOfAcademicInterest
chosen
Indicates that an entity has a particular academic field or subject area as its focus of interest or study.
-
D.
primaryInterest
Indicates that one entity is the main or most significant focus of attention, concern, or engagement for another entity.
-
E.
academicSelection
Indicates a relationship where an entity chooses or designates another entity for an academic purpose, role, or opportunity.
- 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_69c6995703e0819081de77361b602e78 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7048b0b448190889bd40e0a38e51a |
completed | March 27, 2026, 10:28 p.m. |
| PD | Predicate disambiguation | batch_69c701618d3481908be84b76f36ac5a1 |
completed | March 27, 2026, 10:14 p.m. |
Created at: March 27, 2026, 4:01 p.m.