Triple
T37976561
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mintz, Levin, Cohn, Ferris, Glovsky and Popeo |
E947439
|
entity |
| Predicate | hasNumberOfAttorneys |
P56026
|
FINISHED |
| Object | over 400 |
—
|
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: over 400 | Statement: [Mintz, Levin, Cohn, Ferris, Glovsky and Popeo, hasNumberOfAttorneys, over 400]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfAttorneys Context triple: [Mintz, Levin, Cohn, Ferris, Glovsky and Popeo, hasNumberOfAttorneys, over 400]
-
A.
numberOfLawyers
chosen
Indicates the quantity of lawyers associated with a given entity or situation.
-
B.
attorneysAre
Indicates that one or more entities serve in the role or capacity of attorneys for another entity or within a specified context.
-
C.
hasCounsel
Indicates that one entity serves as a legal advisor or representative (counsel) for another entity.
-
D.
hasNumberOfJurors
Indicates the relationship specifying how many jurors are associated with a given legal case, trial, or proceeding.
-
E.
numberOfIndividualsRepresented
Indicates the count of distinct individuals that are represented or spoken for by a given entity or record.
- 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_69f76ef7db908190bba6086673a32300 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69ff397e19a88190a945b826159f5290 |
completed | May 9, 2026, 1:41 p.m. |
| PD | Predicate disambiguation | batch_69ff392400d0819088d30d08d4a774bd |
completed | May 9, 2026, 1:39 p.m. |
Created at: May 3, 2026, 4:20 p.m.