Triple
T5734390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tyrnyauz |
E126463
|
entity |
| Predicate | primaryEconomicSectorPast |
P19843
|
FINISHED |
| Object | non-ferrous metallurgy |
—
|
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: non-ferrous metallurgy | Statement: [Tyrnyauz, primaryEconomicSectorPast, non-ferrous metallurgy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryEconomicSectorPast Context triple: [Tyrnyauz, primaryEconomicSectorPast, non-ferrous metallurgy]
-
A.
formerEconomicActivity
chosen
Indicates that an entity previously engaged in a specified economic activity but no longer does so.
-
B.
economicSectorSourceOfWealth
Indicates that a particular economic sector is the primary source from which an entity derives its wealth or income.
-
C.
hasRuralEconomySector
Indicates that an entity participates in, contains, or is associated with an economic sector based on rural activities or rural development.
-
D.
primaryTrade
Indicates that the referenced activity or exchange is the main or most significant trade relationship associated with the entities involved.
-
E.
earlyOccupation
Indicates that an entity held a particular occupation or job during an early stage of its life or career.
- 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_69c0083082288190b7478cead6b5430a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c029014588819094a2a0f6f9b66bab |
completed | March 22, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c021c6488881909bed4a4534d57f70 |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:47 p.m.