Triple
T33106073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German trade unions |
E847192
|
entity |
| Predicate | sectoralExample |
P176113
|
FINISHED |
| Object | IG Metall |
—
|
NE NERFINISHED |
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: IG Metall | Statement: [German trade unions, sectoralExample, IG Metall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sectoralExample Context triple: [German trade unions, sectoralExample, IG Metall]
-
A.
sectoralCoverage
Indicates the specific sectors, industries, or domains to which something (such as a policy, agreement, or dataset) applies or extends.
-
B.
sectoralClassification
Indicates how an entity is categorized into a specific economic or industry sector within a classification scheme.
-
C.
economicSectors
Indicates a relationship that associates entities with the economic sectors or industries in which they operate or to which they belong.
-
D.
sector
Indicates that an entity operates in, belongs to, or is associated with a particular economic or industrial sector.
-
E.
sectoralVariation
Indicates differences or changes that occur across distinct sectors, fields, or domains within a broader system or context.
- 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_69f3495686508190b76bf20fa5e00bf7 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6db6af1d88190989810182354d60f |
completed | May 3, 2026, 5:21 a.m. |
| PD | Predicate disambiguation | batch_69f6d82d068c8190940a3200ed760e38 |
completed | May 3, 2026, 5:07 a.m. |
| PDg | Predicate description generation | batch_69f6db6a38d881909ecc75cc527910f2 |
completed | May 3, 2026, 5:21 a.m. |
Created at: May 1, 2026, 1:26 a.m.