Triple
T6542402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xcel Energy |
E168321
|
entity |
| Predicate | numberOfCustomers |
P33576
|
FINISHED |
| Object | millions of customers |
—
|
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: millions of customers | Statement: [Xcel Energy, numberOfCustomers, millions of customers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCustomers Context triple: [Xcel Energy, numberOfCustomers, millions of customers]
-
A.
hasCustomers
chosen
Indicates that an entity maintains a business relationship in which other entities purchase or receive its goods or services as customers.
-
B.
estimatedMemberCount
Indicates the approximate or predicted number of members associated with an entity.
-
C.
guestCountApproximate
Indicates that the number of guests involved is represented as an estimated or approximate count rather than an exact figure.
-
D.
userCount
Indicates the number of users associated with or involved in a given context or entity.
-
E.
numberOfOrders
Indicates the total count of orders associated with a given entity or context.
- 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_69c68a51564081909e93aee0dbd9cca3 |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6ce07332481909a5a7964282eb776 |
completed | March 27, 2026, 6:35 p.m. |
| PD | Predicate disambiguation | batch_69c6acf3e3708190b052ec774e607cb7 |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:50 p.m.