Triple
T3501383
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State Farm Insurance |
E73975
|
entity |
| Predicate | numberOfAgents |
P48532
|
FINISHED |
| Object | over 19,000 |
—
|
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 19,000 | Statement: [State Farm Insurance, numberOfAgents, over 19,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAgents Context triple: [State Farm Insurance, numberOfAgents, over 19,000]
-
A.
numberOfHosts
Indicates the total count of distinct hosts associated with or involved in a given entity or event.
-
B.
numberOfInstances
Indicates the quantity or count of distinct occurrences or instances associated with a given entity or context.
-
C.
numberOfAppointedMembers
Indicates the specific count of members who have been formally appointed to a group, body, or position.
-
D.
numberOfDelegates
Indicates the quantity of delegates associated with or assigned to a particular entity or event.
-
E.
numberOfTargets
Indicates the quantity of target entities associated with or affected by a given subject or event.
- 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_69ad85cdb6e48190a335d412b9194ed8 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbbd5fbe8819091b61fa8df355f0c |
completed | March 8, 2026, 6:11 p.m. |
| PD | Predicate disambiguation | batch_69adae0cd8b0819099da300af09880da |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adaef1037c819082c7af949ec85360 |
completed | March 8, 2026, 5:16 p.m. |
Created at: March 8, 2026, 3:18 p.m.