Triple
T3323160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | On Crimes and Punishments |
E69844
|
entity |
| Predicate | positionOnDeathPenalty |
P47278
|
FINISHED |
| Object | abolitionist |
—
|
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: abolitionist | Statement: [On Crimes and Punishments, positionOnDeathPenalty, abolitionist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: positionOnDeathPenalty Context triple: [On Crimes and Punishments, positionOnDeathPenalty, abolitionist]
-
A.
punishmentLocation
Indicates the place or setting where a punishment is carried out or imposed.
-
B.
deathPeninsula
Indicates a relationship where a death occurs on, near, or in association with a specific peninsula.
-
C.
positionOn
Indicates that one entity is located on top of or at a specific place along the surface or extent of another entity.
-
D.
positionOnCrime
Indicates a stance, opinion, or policy position that an entity holds regarding crime or crime-related issues.
-
E.
positionInState
Indicates that one entity occupies or holds a specific position, role, or status within a particular state or condition of another entity.
- 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_69ad85a1829881908942c14075644d0d |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb13e6c288190805f8abcd3acc906 |
completed | March 8, 2026, 5:26 p.m. |
| PD | Predicate disambiguation | batch_69ada42a19348190a3862ce02451f4aa |
completed | March 8, 2026, 4:30 p.m. |
| PDg | Predicate description generation | batch_69ada52716ec81908e89688a81039394 |
completed | March 8, 2026, 4:34 p.m. |
Created at: March 8, 2026, 3:11 p.m.