Triple
T13601712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Washington Mutual |
E324957
|
entity |
| Predicate | hadNumberOfEmployees |
P80887
|
FINISHED |
| Object | over 40,000 (approximate, mid-2000s) |
—
|
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 40,000 (approximate, mid-2000s) | Statement: [Washington Mutual, hadNumberOfEmployees, over 40,000 (approximate, mid-2000s)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadNumberOfEmployees Context triple: [Washington Mutual, hadNumberOfEmployees, over 40,000 (approximate, mid-2000s)]
-
A.
hasEmployees
Indicates that one entity employs one or more other entities as its workers or staff.
-
B.
hadNumberOfMembers
Indicates that an entity possessed or was associated with a specific count of members at a given time or in a given context.
-
C.
hasNumberOfCompanies
Indicates the quantitative relationship specifying how many companies are associated with a given entity.
-
D.
numberOfEmployeesAtPeak
chosen
Indicates the highest recorded count of employees that an entity had at any point in time.
-
E.
numberOfEmployeesDate
Indicates the specific date on which the recorded number of employees for an entity is valid or measured.
- 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_69d80769eaf081909d82f44e484d6113 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb07ad3f48190a2173e42c5cfedb1 |
completed | April 12, 2026, 2:47 p.m. |
| PD | Predicate disambiguation | batch_69dbae18eaf48190809e8b365856cde9 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:49 p.m.