Triple
T15098108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chief Technology Officer at Liquid Robotics |
E360592
|
entity |
| Predicate | employerSpecialization |
P63924
|
FINISHED |
| Object | wave‑powered autonomous ocean vehicles |
—
|
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: wave‑powered autonomous ocean vehicles | Statement: [Chief Technology Officer at Liquid Robotics, employerSpecialization, wave‑powered autonomous ocean vehicles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employerSpecialization Context triple: [Chief Technology Officer at Liquid Robotics, employerSpecialization, wave‑powered autonomous ocean vehicles]
-
A.
occupationType
Indicates the specific kind or category of work, profession, or role that an entity performs or holds.
-
B.
employerFocus
Indicates that an employer directs particular attention, resources, or priority toward a specific subject, group, or area.
-
C.
positionSpecialization
Indicates that one position is a more specialized or focused variant of another, broader position.
-
D.
associatedCompanySpecialization
chosen
Indicates that a company is linked to a particular area of specialization or expertise.
-
E.
professionalCategory
Indicates the classification of an entity according to its professional field, role, or occupational domain.
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0054f00388190a5123d9f4a869b96 |
completed | April 15, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69deb9645b9c8190a5712456dbd78029 |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:04 a.m.