Triple
T9839455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Onion Field |
E239183
|
entity |
| Predicate | portraysRealEvent |
P6686
|
FINISHED |
| Object | 1963 kidnapping of two LAPD officers |
—
|
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: 1963 kidnapping of two LAPD officers | Statement: [The Onion Field, portraysRealEvent, 1963 kidnapping of two LAPD officers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysRealEvent Context triple: [The Onion Field, portraysRealEvent, 1963 kidnapping of two LAPD officers]
-
A.
portraysEvent
chosen
Indicates that one entity depicts, represents, or illustrates a particular event.
-
B.
usesRealHistoricalEvents
Indicates that the subject incorporates or is based on actual events that occurred in real history.
-
C.
hasFictionalEventType
Indicates that something is associated with, characterized by, or classified under a particular type or category of fictional event.
-
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.
basedOnEventsDescribedIn
Indicates that something is derived from, inspired by, or constructed using the events described in another source.
- 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_69ca84e3f0c48190ada72a65ebd50efd |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb34b045481908f89abd576aab497 |
completed | April 2, 2026, 12:07 a.m. |
| PD | Predicate disambiguation | batch_69cd03e30bc08190816c0a6d29c21b0f |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:33 p.m.