Triple
T265529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bitcoin |
E5715
|
entity |
| Predicate | firstBlockMessage |
P8896
|
FINISHED |
| Object | The Times 03/Jan/2009 Chancellor on brink of second bailout for banks |
—
|
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: The Times 03/Jan/2009 Chancellor on brink of second bailout for banks | Statement: [Bitcoin, firstBlockMessage, The Times 03/Jan/2009 Chancellor on brink of second bailout for banks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstBlockMessage Context triple: [Bitcoin, firstBlockMessage, The Times 03/Jan/2009 Chancellor on brink of second bailout for banks]
-
A.
firstMessageTo
Indicates that one entity is the initial sender of a message or communication to another entity.
-
B.
firstMessageFrom
Indicates that the related message is the earliest or initial message sent from one entity to another within a given context or conversation.
-
C.
firstSessionStart
Indicates the point in time when an entity’s very first session or interaction begins.
-
D.
firstNode
Indicates that the subject is the initial or starting node in an ordered sequence, structure, or path.
-
E.
firstModel
Indicates that an entity is the initial or earliest model/version in a sequence or series of models.
- 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_69a2587daeb081909591b9d30f80a271 |
completed | Feb. 28, 2026, 2:52 a.m. |
| NER | Named-entity recognition | batch_69a25d8f9bbc8190a13841e4de093a66 |
completed | Feb. 28, 2026, 3:14 a.m. |
| PD | Predicate disambiguation | batch_69a25b6f60b081908fc6467800a8849e |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25c8ae480819094f6d1bb0a6d2eb2 |
completed | Feb. 28, 2026, 3:10 a.m. |
Created at: Feb. 28, 2026, 2:56 a.m.