Triple
T6140087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buffalo Bill (Jame Gumb) |
E136938
|
entity |
| Predicate | keepsVictimIn |
P69404
|
FINISHED |
| Object | dry well in his basement |
—
|
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: dry well in his basement | Statement: [Buffalo Bill (Jame Gumb), keepsVictimIn, dry well in his basement]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: keepsVictimIn Context triple: [Buffalo Bill (Jame Gumb), keepsVictimIn, dry well in his basement]
-
A.
isVictimOf
Indicates that one entity suffers harm, loss, or wrongdoing as a result of another entity’s actions or events.
-
B.
coVictim
Indicates that two or more entities are victims in the same harmful event or incident.
-
C.
portraysAsVictim
Indicates that one entity represents or depicts another entity as a victim in a given context or narrative.
-
D.
hasVictims
Indicates that an entity has one or more individuals who have been harmed, injured, or adversely affected by it.
-
E.
hasVictimCount
Indicates the number of victims associated with a particular event, action, or 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_69c008a179388190a3b5a081bbf46d55 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05cb030fc8190b78e4967eea65611 |
completed | March 22, 2026, 9:18 p.m. |
| PD | Predicate disambiguation | batch_69c055f19b0c81908be34a00ab218723 |
completed | March 22, 2026, 8:49 p.m. |
| PDg | Predicate description generation | batch_69c056c87340819088003f427706ebf8 |
completed | March 22, 2026, 8:53 p.m. |
Created at: March 22, 2026, 4:15 p.m.