Triple
T7893770
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ZipRecruiter |
E183297
|
entity |
| Predicate | hasCEO |
P2568
|
FINISHED |
| Object | Ian Siegel |
E714864
|
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: Ian Siegel | Statement: [ZipRecruiter, hasCEO, Ian Siegel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ian Siegel Context triple: [ZipRecruiter, hasCEO, Ian Siegel]
-
A.
Ian Siegel
chosen
Ian Siegel is an American entrepreneur best known as the co-founder and longtime CEO of the online employment marketplace ZipRecruiter.
-
B.
Neil Siegel
Neil Siegel is a prominent American legal scholar known for his work in constitutional law and theory, including the study of judicial behavior and the separation of powers.
-
C.
J. David Siegel
J. David Siegel is a film editor known for his work on major animated features, including the superhero comedy "DC League of Super-Pets."
-
D.
Steven Baigelman
Steven Baigelman is an American screenwriter and producer known for his work on biographical and crime dramas in film and television.
-
E.
Alan Siegel
Alan Siegel is a film producer best known for his long-running collaboration with actor Gerard Butler on action and thriller movies.
- 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_69ca828c474c8190a254d6499871eaff |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a008fb88190a039fec40483ab93 |
completed | March 31, 2026, 3:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccec781ac88190b52305beaa213415 |
completed | April 1, 2026, 9:59 a.m. |
Created at: March 30, 2026, 5:01 p.m.