Triple
T9741674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jobs (2013 film) |
E236199
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object | Jobs |
E318140
|
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: Jobs | Statement: [Jobs (2013 film), title, Jobs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jobs Context triple: [Jobs (2013 film), title, Jobs]
-
A.
Jobs
Jobs is the surname of Steve Jobs, the influential co-founder and longtime leader of Apple Inc.
-
B.
Jobs
chosen
Jobs is a 2013 biographical drama film depicting the life and career of Apple co-founder Steve Jobs.
-
C.
jobs aggregator company Jobsinthemoney
Jobsinthemoney is a niche job search platform focused on finance and investment careers, co-founded by entrepreneur Rony Kahan.
-
D.
Jobsinthemoney
Jobsinthemoney was a niche online job board focused on careers in finance and accounting.
-
E.
workforce1 Career Centers network
The Workforce1 Career Centers network is a system of New York City career centers that connect job seekers with employment opportunities, training, and related workforce development services.
- 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_69ca84d3e24481908a476e2231123cf9 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9f2af3e48190b83a442cd0e84062 |
completed | April 1, 2026, 10:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1aff2339c8190b164b13b54a40cec |
completed | April 5, 2026, 12:42 a.m. |
Created at: March 30, 2026, 8:23 p.m.