Triple
T10225301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liv Rundgren Tyler |
E243188
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Liv |
E243189
|
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: Liv | Statement: [Liv Rundgren Tyler, givenName, Liv]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Liv Context triple: [Liv Rundgren Tyler, givenName, Liv]
-
A.
Liv
chosen
Liv is a feminine given name, often used in Scandinavian countries and popularized internationally by actress Liv Tyler.
-
B.
Liv Lo
Liv Lo is a Taiwanese television host, fitness entrepreneur, and yoga instructor known for her work in wellness and as the wife of actor Henry Golding.
-
C.
Lou
Lou is a character from the virtual reality co-op shooter game "After the Fall," set in a post-apocalyptic, frozen Los Angeles overrun by mutated creatures.
-
D.
Lou
Lou is a recurring Springfield police officer on the animated television series "The Simpsons," known as Chief Wiggum’s level-headed, deadpan partner.
-
E.
Lou
Lou is a central character in the Canadian romantic drama film "Take This Waltz," which explores themes of love, fidelity, and emotional restlessness.
- 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_69d381b0f97c819085c9b45799a5fb7c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d1f9cf6c81909a6b9e9b9d0a79fe |
completed | April 7, 2026, 9:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6a84c80c88190846a096ae7c1d7e4 |
completed | April 8, 2026, 7:11 p.m. |
Created at: April 6, 2026, 11:17 a.m.