Triple
T18285372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Heidi Chronicles |
E437967
|
entity |
| Predicate | follows |
P134
|
FINISHED |
| Object | Heidi Holland |
—
|
NE NERFINISHED |
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: Heidi Holland | Statement: [The Heidi Chronicles, follows, Heidi Holland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Heidi Holland Context triple: [The Heidi Chronicles, follows, Heidi Holland]
-
A.
Heidi Holland
chosen
Heidi Holland is the introspective, feminist art historian who serves as the central figure navigating changing social and gender politics in Wendy Wasserstein’s Pulitzer Prize–winning play *The Heidi Chronicles*.
-
B.
Heidi Gardner
Heidi Gardner is an American comedian and actress best known as a cast member on "Saturday Night Live."
-
C.
Heidi Henderson
Heidi Henderson is known as the former spouse of the late American actor William Hurt.
-
D.
Meg Haston
Meg Haston is an American author best known for her middle-grade and young adult novels, including the book that inspired the Nickelodeon television series "How to Rock."
-
E.
Heather Matarazzo
Heather Matarazzo is an American actress best known for her character roles in films like "Welcome to the Dollhouse" and "The Princess Diaries."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b914530c8190b4474d862a2b2a1b |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e500f913d48190b41a1e37ca05e8b1 |
completed | April 19, 2026, 4:21 p.m. |
Created at: April 10, 2026, 10:35 a.m.