Triple
T17031184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Woody Harrelson |
E413196
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object | Deni Harrelson |
E413196
|
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: Deni Harrelson | Statement: [Woody Harrelson, child, Deni Harrelson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deni Harrelson Context triple: [Woody Harrelson, child, Deni Harrelson]
-
A.
Deni Harrelson
chosen
Deni Harrelson is one of the daughters of American actor Woody Harrelson.
-
B.
Teri Hudson
Teri Hudson is the wife of Stanley Hudson, a character from the American television series "The Office."
-
C.
Dena Kaye
Dena Kaye is the daughter of famed American entertainer Danny Kaye and has been involved in preserving and promoting her father's legacy.
-
D.
Kelley O'Hara
Kelley O'Hara is an American professional soccer player and World Cup–winning defender known for her versatility and long tenure with the U.S. women’s national team.
-
E.
Laura Harrington
Laura Harrington is an American actress best known for her role in the 1986 Stephen King film "Maximum Overdrive."
- 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_69d886cd18288190b006abab23f811b7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d5d9e7d481909d3d5bd241bd68f1 |
completed | April 18, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a011b5748bc8190832737a70219e7a1 |
completed | May 10, 2026, 11:57 p.m. |
Created at: April 10, 2026, 5:33 a.m.