Triple
T32498299
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Message-Digest Algorithm 5 |
E830584
|
entity |
| Predicate | chosenPrefixCollisionDemonstratedYear |
P131143
|
FINISHED |
| Object | 2007 |
—
|
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: 2007 | Statement: [Message-Digest Algorithm 5, chosenPrefixCollisionDemonstratedYear, 2007]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: chosenPrefixCollisionDemonstratedYear Context triple: [Message-Digest Algorithm 5, chosenPrefixCollisionDemonstratedYear, 2007]
-
A.
chosenPrefixCollisionYear
chosen
Indicates the year in which a specific prefix was selected or identified as having a collision with another prefix.
-
B.
firstCollisionsYear
Indicates the year in which the first collisions or interaction events occurred between the relevant entities.
-
C.
claimedYear
Indicates the year that is asserted or reported as being associated with an event, status, or fact, regardless of whether it is verified.
-
D.
hasCounterexampleYear
Indicates the year in which a counterexample to a claim, conjecture, or statement was found or demonstrated.
-
E.
previousConflict
Indicates that a conflict or dispute occurred between the entities at some earlier time prior to the current context.
- 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_69f349219cb8819087e120f509629c1b |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c4409dc48190a9eac031b88571a1 |
completed | May 3, 2026, 3:42 a.m. |
| PD | Predicate disambiguation | batch_69f6bd2a14b081908162923dfbf0a6f4 |
completed | May 3, 2026, 3:12 a.m. |
Created at: May 1, 2026, 12:59 a.m.