Triple
T32304684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Treuhandanstalt |
E825330
|
entity |
| Predicate | numberOfEmployeesAffected |
P54084
|
FINISHED |
| Object | millions |
—
|
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: millions | Statement: [Treuhandanstalt, numberOfEmployeesAffected, millions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfEmployeesAffected Context triple: [Treuhandanstalt, numberOfEmployeesAffected, millions]
-
A.
estimatedAffectedPeople
chosen
Indicates the estimated number of people expected to be impacted by a particular event, condition, or action.
-
B.
affectedPeople
Indicates the people who are impacted or influenced by a particular event, action, or condition.
-
C.
numberOfWorkersFired
Indicates the quantity of workers who were dismissed or terminated from their jobs.
-
D.
standAffected
Indicates that an entity is in a state or position of being impacted or influenced by another entity or event.
-
E.
numberOfEmployeesDate
Indicates the specific date on which the recorded number of employees for an entity is valid or measured.
- 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_69f349115304819084ee91d345b6c8aa |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a01a23d8b148190ac2c8765aa9227c4 |
completed | May 11, 2026, 9:32 a.m. |
| PD | Predicate disambiguation | batch_6a01a1e8bb90819096647e929bfb3db8 |
completed | May 11, 2026, 9:31 a.m. |
Created at: May 1, 2026, 12:45 a.m.