Triple
T3504073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jay Last |
E74033
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Teledyne |
E236227
|
NE 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: Teledyne | Statement: [Jay Last, employer, Teledyne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teledyne Context triple: [Jay Last, employer, Teledyne]
-
A.
Teledyne
chosen
Teledyne is an American industrial conglomerate known for its diversified operations in electronics, instrumentation, aerospace, and digital imaging technologies.
-
B.
FLIR Systems
FLIR Systems is a leading American company specializing in the design and manufacture of thermal imaging cameras, sensors, and infrared imaging technologies used in defense, industrial, and commercial applications.
-
C.
Fluke Corporation
Fluke Corporation is a leading American manufacturer of electronic test tools and software, widely used in industrial, electrical, and calibration applications.
-
D.
Agilent Technologies
Agilent Technologies is a global company specializing in life sciences, diagnostics, and analytical laboratory instruments and services.
-
E.
Kavlico Corporation
Kavlico Corporation is a technology company known for designing and manufacturing pressure, position, and force sensors for aerospace, industrial, and automotive applications.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ad85ce7a9c81909ddc5cf0cb67a6e3 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbbf22b1c8190956141d8fb924210 |
completed | March 8, 2026, 6:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b373de0a34819096701e24409a08bb |
completed | March 13, 2026, 2:18 a.m. |
Created at: March 8, 2026, 3:18 p.m.