Triple
T5372803
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daimler-Benz DB 606 |
E108890
|
entity |
| Predicate | numberOfBanks |
P63611
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Daimler-Benz DB 606, numberOfBanks, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfBanks Context triple: [Daimler-Benz DB 606, numberOfBanks, 4]
-
A.
numberOfFederalReserveBanks
Indicates the quantity of Federal Reserve Banks associated with or relevant to a given entity.
-
B.
numberOfATMs
Indicates the quantitative relationship specifying how many ATMs are associated with a given entity or location.
-
C.
numberOfTargetInstitutions
Indicates the count of institutions that are designated or identified as targets in a given context or dataset.
-
D.
branchCount
Indicates the number of branches associated with a given entity or structure.
-
E.
hasBank
Indicates that one entity possesses, is associated with, or is served by a particular bank (such as a financial institution or river bank).
- 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_69bd440c77948190aad2a5f39b7b80f5 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd88801b188190b9ac35ed89167fa3 |
completed | March 20, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69bd846172788190969f24bc7503c05e |
completed | March 20, 2026, 5:31 p.m. |
| PDg | Predicate description generation | batch_69bd887f5b0081909456992d1a071928 |
completed | March 20, 2026, 5:48 p.m. |
Created at: March 20, 2026, 2:03 p.m.