Triple
T8674575
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Victor Horta |
E205881
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Victor |
E30470
|
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: Victor | Statement: [Victor Horta, givenName, Victor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Victor Context triple: [Victor Horta, givenName, Victor]
-
A.
Victor
Victor is a central character in the TV series "Dollhouse," known as one of the programmable "Actives" whose identity and memories are repeatedly altered for various missions.
-
B.
Victor
chosen
Victor is a masculine given name of Latin origin meaning "conqueror" or "winner," commonly used in many European and English-speaking countries.
-
C.
Victor
Victor was a prominent early 20th-century record label known for producing and distributing influential jazz and popular music recordings.
-
D.
Víctor
Víctor is a given name commonly used in Spanish-speaking countries, derived from the Latin name Victor meaning "winner" or "conqueror."
-
E.
Viktor
Viktor is a powerful and ancient vampire elder from the "Underworld" film series, portrayed by actor Bill Nighy.
- 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_69ca83529a9c8190b5c075b4f14636ed |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc491cee048190a6f6cddabada76dc |
completed | March 31, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cecd43c91c8190a9d1e5d14d764530 |
completed | April 2, 2026, 8:10 p.m. |
Created at: March 30, 2026, 6:31 p.m.