Triple
T2245232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ilsa Lund |
E49486
|
entity |
| Predicate | notableQuoteContext |
P492
|
FINISHED |
| Object | involved in the line "We'll always have Paris" |
—
|
LITERAL 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: involved in the line "We'll always have Paris" | Statement: [Ilsa Lund, notableQuoteContext, involved in the line "We'll always have Paris"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableQuoteContext Context triple: [Ilsa Lund, notableQuoteContext, involved in the line "We'll always have Paris"]
-
A.
notableQuote
chosen
Indicates that one entity is a significant or well-known quotation attributed to, recorded by, or strongly associated with another entity.
-
B.
notableQuoteTranslation
Indicates that one quote is a translation of another quote, preserving its meaning across different languages.
-
C.
notableFor
Indicates that an entity is especially recognized or distinguished for a particular quality, achievement, characteristic, or role.
-
D.
notableFact
Indicates that there exists a particularly significant or noteworthy fact or piece of information associated with the subject.
-
E.
quoteProvision
Indicates that one entity supplies or presents a quotation or price estimate to another entity.
- F. None of above.
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_69a88aa979788190ad6500f1d8eee2fc |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc0e8d5648190915ff689c7ca42bc |
completed | March 7, 2026, 6:08 a.m. |
| PD | Predicate disambiguation | batch_69abbdb160248190aa75b38f11ad8602 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:47 p.m.