Triple
T5775244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pleistrus |
E127423
|
entity |
| Predicate | maternalAunt |
P47317
|
FINISHED |
| Object | Timandra |
E149047
|
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: Timandra | Statement: [Pleistrus, maternalAunt, Timandra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Timandra Context triple: [Pleistrus, maternalAunt, Timandra]
-
A.
Timandra
chosen
Timandra is a figure from Greek mythology, traditionally considered one of the daughters of Tyndareus and Leda and thus a member of the same royal Spartan family as Helen of Troy and Clytemnestra.
-
B.
Tarana
Tarana is the first name of Tarana Burke, the American civil rights activist who founded the Me Too movement.
-
C.
Stryama
Stryama is a river in Bulgaria that flows through the central part of the country before joining the Maritsa River.
-
D.
Telaria
Telaria was a video advertising and monetization technology company specializing in connected TV and premium video inventory.
-
E.
Galanta
Galanta is a town in southern Slovakia known as a regional center with historical ties to the Esterházy noble family.
- 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_69c008361fa88190aefa4dc41b051e7f |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0335f23c081909b35020801e3ef12 |
completed | March 22, 2026, 6:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07e6bf6348190b0ba46253585c7d9 |
completed | March 22, 2026, 11:42 p.m. |
Created at: March 22, 2026, 3:50 p.m.