Triple
T14134480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steve Ferrone |
E350253
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Steve |
E614284
|
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: Steve | Statement: [Steve Ferrone, givenName, Steve]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steve Context triple: [Steve Ferrone, givenName, Steve]
-
A.
Steve
Steve is a central white homeowner character in the play "Clybourne Park," often embodying the tensions and awkward defenses of privilege in the story’s exploration of race and gentrification.
-
B.
Steve
Steve is the familiar given name of Stephen Gary Wozniak, the pioneering American computer engineer and co-founder of Apple Inc.
-
C.
Steve
chosen
Steve is a masculine given name commonly used in English-speaking countries, often as a short form of Stephen or Steven.
-
D.
Steve
Steve is the central protagonist of the adventure story "High Seas," around whom the main events and conflicts of the narrative revolve.
-
E.
Steve
Steve is the central protagonist of the romantic comedy film "Him & Her," around whom the story’s relationships and conflicts revolve.
- 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_69d827865f608190b311820428ae027b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de610e949c8190852d336c9d12bfd0 |
completed | April 14, 2026, 3:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcdf14439c81908b2a9999a35cc346 |
completed | May 7, 2026, 6:51 p.m. |
Created at: April 9, 2026, 11:45 p.m.