Triple
T241543
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Master Settlement Agreement |
E4941
|
entity |
| Predicate | numberOfOriginalStates |
P244
|
FINISHED |
| Object | 46 |
—
|
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: 46 | Statement: [Master Settlement Agreement, numberOfOriginalStates, 46]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfOriginalStates Context triple: [Master Settlement Agreement, numberOfOriginalStates, 46]
-
A.
numberOfStates
chosen
Indicates the total count of distinct states or conditions associated with an entity or system.
-
B.
numberOfProvinces
Indicates the total count of provinces associated with a given entity or within a specified region or country.
-
C.
numberOfStages
Indicates the total count of distinct stages or phases associated with a given process, event, or entity.
-
D.
historicalState
Indicates that an entity existed in a particular state or condition during a specified time in the past.
-
E.
numberOfPositions
Indicates the total count of distinct positions or roles associated with a given entity.
- 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25d35aa288190966b6e15af1525cb |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b60ad308190b12f119960a8bde7 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.