Triple
T8427658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Just a Little More Love |
E199041
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | You |
E812
|
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: You | Statement: [Just a Little More Love, hasPart, You]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: You Context triple: [Just a Little More Love, hasPart, You]
-
A.
You
chosen
"You" refers to the collective community of internet users whose user-generated content and online collaboration transformed media, culture, and communication in the digital age.
-
B.
We
"We" is Charles Lindbergh’s autobiographical account of his historic 1927 solo nonstop flight across the Atlantic and the events surrounding it.
-
C.
Vo
Vo is a Vietnamese surname most famously associated with General Vo Nguyen Giap, a key military leader in Vietnam's 20th-century history.
-
D.
It
"It" is a 1927 silent romantic comedy film that made Clara Bow famous as the original "It Girl" and a major Hollywood star.
-
E.
It
It is a 1986 horror novel by Stephen King about a shape-shifting entity that terrorizes children in the town of Derry, Maine.
- 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_69ca8313c99081909a5c6d83b91de5b3 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbd124378c819086ea2fa6ecbfffe1 |
completed | March 31, 2026, 1:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce036dec9481908564ea2828ea429b |
completed | April 2, 2026, 5:49 a.m. |
Created at: March 30, 2026, 6:07 p.m.