Triple
T26822818
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Murder on K Street |
E675296
|
entity |
| Predicate | seriesNumberInCapitalCrimes |
P102560
|
FINISHED |
| Object | 21 |
—
|
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: 21 | Statement: [Murder on K Street, seriesNumberInCapitalCrimes, 21]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seriesNumberInCapitalCrimes Context triple: [Murder on K Street, seriesNumberInCapitalCrimes, 21]
-
A.
hasSerialKiller
Indicates that one entity is a serial killer associated with, responsible for, or targeting another entity.
-
B.
seriesNumberOfWorks
chosen
Indicates that an entity is assigned a specific position or sequence number within a series of related works.
-
C.
committedCrime
Indicates that an entity has carried out or been responsible for a criminal act or offense.
-
D.
hasSeriesNumber
Indicates that an entity is assigned a specific ordinal or sequence number within a series or ordered set.
-
E.
trackNumberOnJustice
Indicates the position or sequence number that something occupies on the entity referred to as "Justice," such as a track on an album.
- 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_69eee9b6b28481909332f83eb17e5170 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f69383222c81909d8baa04129d5c81 |
completed | May 3, 2026, 12:14 a.m. |
| PD | Predicate disambiguation | batch_69f690eb1e948190aab41a89969519a5 |
completed | May 3, 2026, 12:03 a.m. |
Created at: April 27, 2026, 4:56 a.m.