Triple
T22623121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marg |
E558338
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Maggie |
—
|
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: Maggie | Statement: [Marg, relatedName, Maggie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maggie Context triple: [Marg, relatedName, Maggie]
-
A.
Maggie
"Maggie" is a novel by American author Charles Martin, known for its emotionally driven storytelling and themes of love, loss, and redemption.
-
B.
Maggie
chosen
Maggie is a common diminutive form of the given name Margaret, often used as a familiar or affectionate nickname.
-
C.
Maggie
Maggie is a character portrayed by Australian actress Robin McLeavy, best known from the horror film "The Loved Ones."
-
D.
Maggie
"Maggie" is a 2015 post-apocalyptic drama film starring Arnold Schwarzenegger as a father caring for his daughter during her slow transformation into a zombie.
-
E.
Maggie
Maggie is a tough, self-sacrificing resistance fighter in the dystopian action film "Escape from New York."
- 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_69e24545a8e08190bfa7482a2c725ff1 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f16e3b97f8819082ea2b23ff4b2173 |
completed | April 29, 2026, 2:34 a.m. |
Created at: April 17, 2026, 3:01 p.m.