Triple
T1488925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oak Ridge History Museum |
E29531
|
entity |
| Predicate | thematicArea |
P28568
|
FINISHED |
| Object | World War II history |
—
|
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: World War II history | Statement: [Oak Ridge History Museum, thematicArea, World War II history]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: thematicArea Context triple: [Oak Ridge History Museum, thematicArea, World War II history]
-
A.
commonPolicyArea
Indicates that two entities share the same policy domain, topic, or area of regulatory or legislative focus.
-
B.
isPartOfPolicyArea
Indicates that one policy, topic, or issue belongs to, falls under, or is categorized within a broader policy area or domain.
-
C.
competenceArea
Indicates that one entity has a particular domain, field, or area in which it possesses competence, expertise, or responsibility.
-
D.
keyIssueArea
Indicates that something is a primary topic, domain, or field that is central or especially important within a broader context or discussion.
-
E.
innovationArea
Indicates the thematic or domain-specific field in which an innovation is focused or applied.
- 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_69a498da82e08190ba833330d05f380f |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c6a6095481909e9d406ac9a41828 |
completed | March 1, 2026, 11:07 p.m. |
| PD | Predicate disambiguation | batch_69a4c48902808190a8028d359bcf123e |
completed | March 1, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69a4c52c703c8190a56389b09d97659f |
completed | March 1, 2026, 11:01 p.m. |
Created at: March 1, 2026, 8:12 p.m.