Triple
T13114276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arapesh |
E311052
|
entity |
| Predicate | famousCaseStudyIn |
P2398
|
FINISHED |
| Object | cultural anthropology |
—
|
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: cultural anthropology | Statement: [Arapesh, famousCaseStudyIn, cultural anthropology]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: famousCaseStudyIn Context triple: [Arapesh, famousCaseStudyIn, cultural anthropology]
-
A.
hasCaseStudy
chosen
Indicates that one entity is documented, illustrated, or analyzed by a specific case study associated with it.
-
B.
caseStudyNumber
Indicates the identifying number assigned to a specific case study within a collection or series.
-
C.
landmarkCase
Indicates that a legal case is historically significant or precedent-setting within a legal system.
-
D.
famousVariant
Indicates that one entity is a well-known or widely recognized version or form of another entity.
-
E.
widelyStudiedIn
Indicates that something has been extensively researched, analyzed, or examined within a particular field, domain, or context.
- 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_69d806a872d08190a329806f8ff30df4 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d9817f8ee8819084078b4bec5e4f18 |
completed | April 10, 2026, 11:02 p.m. |
| PD | Predicate disambiguation | batch_69d98041a3548190a05ddd83dbb660fa |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:06 p.m.