Triple
T16034501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wayne the werewolf |
E388933
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Wanda the werewolf |
E834924
|
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: Wanda the werewolf | Statement: [Wayne the werewolf, spouse, Wanda the werewolf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wanda the werewolf Context triple: [Wayne the werewolf, spouse, Wanda the werewolf]
-
A.
Wanda the werewolf
chosen
Wanda the werewolf is a friendly, maternal werewolf character from the Hotel Transylvania film series and one of Mavis Dracula’s closest companions.
-
B.
Wayne the werewolf
Wayne the werewolf is a harried, overworked werewolf dad and one of Dracula’s loyal monster friends in the animated Hotel Transylvania film series.
-
C.
Wanda
Wanda is a feminine given name of Slavic origin, particularly common in Poland and other Central and Eastern European countries.
-
D.
Wanda
Wanda is a fairy godparent character from the animated series "The Fairly OddParents," known for her responsible and level-headed personality.
-
E.
River Were
River Were is a small river in Wiltshire, England, that flows through the town of Warminster and its surrounding countryside.
- 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_69d86dada3808190825d5f80d72fbe88 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1833a5aa88190a5cc3f82d55f5b62 |
completed | April 17, 2026, 12:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffdbd1cafc81909125174eed475d55 |
completed | May 10, 2026, 1:13 a.m. |
Created at: April 10, 2026, 4:56 a.m.