Triple
T25010198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Henkel |
E625959
|
entity |
| Predicate | hasEmployeeNumberApprox |
P17907
|
FINISHED |
| Object | 50000+ |
—
|
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: 50000+ | Statement: [Henkel, hasEmployeeNumberApprox, 50000+]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEmployeeNumberApprox Context triple: [Henkel, hasEmployeeNumberApprox, 50000+]
-
A.
employsApproximateNumberOfPeople
chosen
Indicates that an entity employs a roughly estimated or approximate number of people, rather than an exact headcount.
-
B.
HRNumber
Indicates that an entity is associated with a specific human resources identification number used for personnel or employment records.
-
C.
hrNumber
Indicates a unique human resources identification number assigned to an individual or position within an organization.
-
D.
hasEmployeeRange
Indicates the range or limits on the number of employees associated with an entity.
-
E.
hasApproximateNumberOfPeople
Indicates that an entity is associated with an estimated or approximate count of people, rather than an exact number.
- 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_69e2ff27755881908490178e83701160 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f464b4c9b0819085daa00c7c3b8b76 |
completed | May 1, 2026, 8:30 a.m. |
| PD | Predicate disambiguation | batch_69f45cfb53f4819099bba48c5057e787 |
completed | May 1, 2026, 7:57 a.m. |
Created at: April 18, 2026, 6:05 a.m.