Triple
T5255849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Organisation Todt |
E118693
|
entity |
| Predicate | employedNumberOfWorkersAtPeak |
P17907
|
FINISHED |
| Object | over one million |
—
|
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 one million | Statement: [Organisation Todt, employedNumberOfWorkersAtPeak, over one million]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employedNumberOfWorkersAtPeak Context triple: [Organisation Todt, employedNumberOfWorkersAtPeak, over one million]
-
A.
memberCountAtPeak
Indicates the highest number of members that an entity (such as a group or organization) has had at any point in time.
-
B.
employsApproximateNumberOfPeople
chosen
Indicates that an entity employs a roughly estimated or approximate number of people, rather than an exact headcount.
-
C.
teamCountAtPeak
Indicates the number of team members involved at the highest or peak point of activity, size, or performance.
-
D.
employedPeople
Indicates that there exists a relationship where people are currently working in jobs or positions, typically under an employer.
-
E.
employedApproximately
Indicates that one entity employs another in a manner where the number, duration, or extent of employment is approximate rather than exact.
- 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_69bd446978108190bb5f9c5c23d93f88 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7ba4ecd88190800b5e4eea3abed5 |
completed | March 20, 2026, 4:53 p.m. |
| PD | Predicate disambiguation | batch_69bd77c30bac8190a883ca45da35d667 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:50 p.m.