Triple
T312713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Library of Congress Subject Headings |
E7641
|
entity |
| Predicate | usedWorldwide |
P11850
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Library of Congress Subject Headings, usedWorldwide, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedWorldwide Context triple: [Library of Congress Subject Headings, usedWorldwide, true]
-
A.
usedInCountry
Indicates that something is utilized, applied, or in operation within the specified country.
-
B.
hasGlobalReach
Indicates that an entity’s influence, operations, or impact extends across multiple countries or worldwide.
-
C.
usedUniformlyAcrossCountry
Indicates that something is applied or practiced in the same way throughout the entire country without regional variation.
-
D.
usedInRegion
Indicates that something is utilized or applied within a specific geographic or administrative region.
-
E.
usedInInternationalTrade
Indicates that something participates as a good, service, or instrument in commercial exchanges between different countries.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea4aa16881909b2c8404b85992df |
completed | Feb. 28, 2026, 1:14 p.m. |
| PD | Predicate disambiguation | batch_69a2e9428098819089d5950cd2c96dc4 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea08878c8190a5e8a90f620a3888 |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:07 p.m.