Triple
T12664719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stephen Callaghan |
E302518
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Stephen |
E83906
|
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: Stephen | Statement: [Stephen Callaghan, hasGivenName, Stephen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stephen Context triple: [Stephen Callaghan, hasGivenName, Stephen]
-
A.
Stephen
chosen
Stephen is a masculine given name of Greek origin meaning "crown" or "garland," widely used in English-speaking countries.
-
B.
Stephen
Stephen is the formal given name of Steve Wozniak, the American computer engineer and co-founder of Apple Inc.
-
C.
Stephen
Stephen was the birth name of Pope Stephen II, a 8th-century pontiff who played a key role in forging the alliance between the papacy and the Frankish kingdom.
-
D.
Stephen
Stephen is the central protagonist of the film "Empire of Light," around whom the story’s emotional and social themes revolve.
-
E.
Stephen
Stephen is the given first name of Steve Wynn, the American real estate businessman and art collector known for his role in developing major Las Vegas casinos.
- 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_69d7bded71a88190bb76e2413af9ea66 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9617e030881908444743b8a7e0d75 |
completed | April 10, 2026, 8:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6688a19148190b8d252d3706d2b05 |
completed | May 2, 2026, 9:11 p.m. |
Created at: April 9, 2026, 5:19 p.m.