Triple
T8291804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles Schwab Corporation |
E193914
|
entity |
| Predicate | headcountApproximate |
P10694
|
FINISHED |
| Object | tens of thousands of employees |
—
|
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: tens of thousands of employees | Statement: [Charles Schwab Corporation, headcountApproximate, tens of thousands of employees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: headcountApproximate Context triple: [Charles Schwab Corporation, headcountApproximate, tens of thousands of employees]
-
A.
estimatedMemberCount
Indicates the approximate or predicted number of members associated with an entity.
-
B.
guestCountApproximate
Indicates that the number of guests involved is represented as an estimated or approximate count rather than an exact figure.
-
C.
userCount
Indicates the number of users associated with or involved in a given context or entity.
-
D.
hasPopulationApproximate
Indicates that an entity has an estimated or approximate population size, rather than an exact count.
-
E.
hasHeadCount
chosen
Indicates that an entity is associated with a specific number of individuals, typically representing the size or count of people (or similar units) related to it.
- 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_69ca82e32db481908b72f3804fa71152 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7c9ccbfc81908825685c23b80d23 |
completed | March 31, 2026, 7:49 a.m. |
| PD | Predicate disambiguation | batch_69cb70b5b5348190b296e0ecec95de60 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 5:52 p.m.