Triple
T19666063
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dallas Green |
E472200
|
entity |
| Predicate | associatedAct |
P37
|
FINISHED |
| Object | Pink |
—
|
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: Pink | Statement: [Dallas Green, associatedAct, Pink]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pink Context triple: [Dallas Green, associatedAct, Pink]
-
A.
Pink
chosen
Pink is an American pop-rock singer and songwriter known for her powerful vocals, acrobatic live performances, and hits such as "Just Give Me a Reason" and "So What."
-
B.
Pink
Pink is a light, reddish color often associated with softness, romance, and playfulness.
-
C.
Pink
Pink is a 2016 Indian courtroom drama film that explores themes of consent, patriarchy, and women's rights within the urban middle-class milieu.
-
D.
Pinks
Pinks is a programming language or framework associated with the SPEED project, recognized for its role in performance-oriented software development.
-
E.
PINK
PINK is a youthful, college-age-focused lingerie and loungewear brand known for its playful, colorful designs and casual lifestyle apparel.
- 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_69d8e514f2e08190ba70a4449519d218 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6416857c88190acb3adbf3e585fe5 |
completed | April 20, 2026, 3:08 p.m. |
Created at: April 10, 2026, 1:45 p.m.