Triple
T429194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Messerschmitt Bf 110 |
E9675
|
entity |
| Predicate | approximateNumberBuilt |
P9773
|
FINISHED |
| Object | over 6000 |
—
|
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: over 6000 | Statement: [Messerschmitt Bf 110, approximateNumberBuilt, over 6000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateNumberBuilt Context triple: [Messerschmitt Bf 110, approximateNumberBuilt, over 6000]
-
A.
numberBuilt
Indicates the total count of items or structures that have been constructed or produced.
-
B.
approximates
Indicates that one entity is close to, but not exactly equal to, the value, form, or behavior of another entity.
-
C.
approximationType
Indicates the specific method or scheme used to approximate a value, function, or relationship in a given context.
-
D.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
E.
hasApproximateValue
chosen
Indicates that one entity’s value is close to, but not exactly equal to, the value of another entity within an acceptable margin of error.
- 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_69a2e801e1d48190b505d1dd336b52ac |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eeecb64c81908c5c83ef7c0181e6 |
completed | Feb. 28, 2026, 1:34 p.m. |
| PD | Predicate disambiguation | batch_69a2edd7a3608190b8785c7b7205f6c1 |
completed | Feb. 28, 2026, 1:29 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.