Triple
T4096725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Thief, His Wife and the Canoe |
E87838
|
entity |
| Predicate | portraysConsequence |
P40708
|
FINISHED |
| Object | criminal trial |
—
|
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: criminal trial | Statement: [The Thief, His Wife and the Canoe, portraysConsequence, criminal trial]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysConsequence Context triple: [The Thief, His Wife and the Canoe, portraysConsequence, criminal trial]
-
A.
hasConsequence
Indicates that one event, action, or condition leads to or results in another as its outcome or effect.
-
B.
narrativeConsequence
chosen
Indicates that one event, action, or state occurs as a direct result or outcome of another within a narrative sequence.
-
C.
violationConsequences
Indicates the negative outcomes, penalties, or repercussions that result from a violation of a rule, law, or agreement.
-
D.
portrayalLedTo
Indicates that one entity’s portrayal of another caused or significantly contributed to a subsequent outcome, reaction, or state involving that other entity.
-
E.
portraysAdversary
Indicates that one entity depicts or represents another entity as an opponent, enemy, or rival.
- 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_69aed94564cc8190a9c1457daedb6e7f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefcdef11081908c626a89f2c0e121 |
completed | March 9, 2026, 5:01 p.m. |
| PD | Predicate disambiguation | batch_69aef90b2ef08190ae84febfd69dd48b |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:40 p.m.