Triple
T5110111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Titus Andronicus |
E115192
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Tamora |
E493814
|
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: Tamora | Statement: [Titus Andronicus, hasCharacter, Tamora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tamora Context triple: [Titus Andronicus, hasCharacter, Tamora]
-
A.
Tamora
chosen
Tamora is the vengeful Queen of the Goths and a central antagonist in William Shakespeare’s tragedy "Titus Andronicus."
-
B.
Channah
Channah is a given name, often considered a variant of the Hebrew name Hannah, traditionally associated with grace or favor.
-
C.
Queen Bavmorda
Queen Bavmorda is the ruthless and power-hungry sorceress-queen who serves as the primary villain in the fantasy film "Willow."
-
D.
Morgause
Morgause is a prominent figure in Arthurian legend, often depicted as a queen of Orkney and the mother of several of King Arthur’s nephews, including Gawain.
-
E.
Sorsha
Sorsha is a warrior princess from the fantasy film "Willow" who initially serves her evil mother Queen Bavmorda before ultimately turning against her.
- 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_69bd4441d1648190a54a533895041987 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd75ac19e88190aa8cc8b930a58d2d |
completed | March 20, 2026, 4:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bec372e308819082fefe9e2b58370d |
completed | March 21, 2026, 4:12 p.m. |
Created at: March 20, 2026, 1:41 p.m.