Triple
T78828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ich bin ein Berliner speech |
E1580
|
entity |
| Predicate | approximateAudienceSize |
P3846
|
FINISHED |
| Object | 450000 |
—
|
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: 450000 | Statement: [Ich bin ein Berliner speech, approximateAudienceSize, 450000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateAudienceSize Context triple: [Ich bin ein Berliner speech, approximateAudienceSize, 450000]
-
A.
hasPopulationApproximate
Indicates that an entity has an estimated or approximate population size, rather than an exact count.
-
B.
hasApproximateTotalSpeakers
Indicates that an entity is associated with an estimated or roughly calculated number of total speakers, rather than an exact count.
-
C.
passengersCountApproximate
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
-
D.
hasApproximateNativeSpeakers
Indicates that an entity is associated with an estimated or approximate number of people who speak it as their native language.
-
E.
hasAudience
Indicates that an entity is intended to be received, viewed, or engaged with by a particular group of people.
- 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_69a24c60d19c8190a1b6c105ca59ef5b |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a24fd16c248190a6ee4cd96c388772 |
completed | Feb. 28, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69a24eb126b48190b410b859c1be99aa |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a24fcf5a88819088c5fa4c08476358 |
completed | Feb. 28, 2026, 2:15 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.