Triple
T27911216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Renaud Laplanche |
E705932
|
entity |
| Predicate | previousCompany |
P1910
|
FINISHED |
| Object | TripleHop Technologies |
—
|
NE NERFINISHED |
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: TripleHop Technologies | Statement: [Renaud Laplanche, previousCompany, TripleHop Technologies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: previousCompany Context triple: [Renaud Laplanche, previousCompany, TripleHop Technologies]
-
A.
formerEmployer
chosen
Indicates that one entity previously employed the other but no longer does so.
-
B.
previousCorporateAffiliation
Indicates that an entity was formerly employed by, associated with, or part of a specified corporate organization before its current status or affiliation.
-
C.
previousCompanyFocus
Indicates that a company previously concentrated its business activities or strategic efforts on a particular industry, market, or area of specialization.
-
D.
employerPredecessorName
Indicates that the referenced name identifies a previous employer of the entity in question.
-
E.
previousDepartment
Indicates that an entity was formerly associated with or belonged to a specified department before a change occurred.
- 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_69ef96b5aad08190be36a277c31e7004 |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69fcda3699948190adb57625bae08091 |
completed | May 7, 2026, 6:30 p.m. |
| PD | Predicate disambiguation | batch_69fcd8fd16d08190b0aca6e19a632e99 |
completed | May 7, 2026, 6:25 p.m. |
Created at: April 27, 2026, 6:50 p.m.