Triple
T12929700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leland McKenzie |
E309341
|
entity |
| Predicate | lawFirmType |
P107070
|
FINISHED |
| Object | full-service Los Angeles law firm |
—
|
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: full-service Los Angeles law firm | Statement: [Leland McKenzie, lawFirmType, full-service Los Angeles law firm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lawFirmType Context triple: [Leland McKenzie, lawFirmType, full-service Los Angeles law firm]
-
A.
lawFirm
Indicates a relationship where one entity is a law firm that provides legal services or representation to another entity.
-
B.
typeOfLaw
Indicates that one entity is a specific category or kind of law to which the other entity pertains.
-
C.
lawFirmNameInSeries
Indicates that a particular law firm is known by a specified name within a given series or franchise.
-
D.
legalPractice
Indicates a relationship where an entity engages in or is associated with the professional provision of legal services or the practice of law.
-
E.
typicalLegalForm
Indicates the standard or commonly used legal organizational form associated with an 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_69d7bdfa933c8190b5a27aa4a08a19b7 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971ec72a48190aceef10630603d2c |
completed | April 10, 2026, 9:55 p.m. |
| PD | Predicate disambiguation | batch_69d96fab4d0881909a7a4d66bab9aa85 |
completed | April 10, 2026, 9:46 p.m. |
| PDg | Predicate description generation | batch_69d970f6f5748190ad35aff801db53d5 |
completed | April 10, 2026, 9:51 p.m. |
Created at: April 9, 2026, 5:42 p.m.