Triple
T615139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Los Angeles Police Department |
E12186
|
entity |
| Predicate | badgeNumberSystem |
P3378
|
FINISHED |
| Object | serial numbers for 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: serial numbers for officers | Statement: [Los Angeles Police Department, badgeNumberSystem, serial numbers for officers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: badgeNumberSystem Context triple: [Los Angeles Police Department, badgeNumberSystem, serial numbers for officers]
-
A.
badge
Indicates that one entity confers, displays, or is associated with a symbolic mark or emblem representing status, achievement, role, or affiliation in relation to another entity.
-
B.
pennantNumber
Indicates the identifying pennant number assigned to a ship or naval vessel.
-
C.
jerseyNumber
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
-
D.
badgeReverseDesign
Indicates the design or imagery that appears on the reverse (back) side of a badge.
-
E.
numberingType
chosen
Indicates the scheme or style used to assign sequential numbers or labels within an ordered set.
- 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_69a493309df48190a327f748e88049a6 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49e0b438881909ad515adf7a4eb79 |
completed | March 1, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69a49cfbcbf88190a854921dc531eba8 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:35 p.m.