Triple
T8129922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 13 Going on 30 |
E189827
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Gina Matthews |
E722520
|
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: Gina Matthews | Statement: [13 Going on 30, producer, Gina Matthews]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gina Matthews Context triple: [13 Going on 30, producer, Gina Matthews]
-
A.
Gina Matthews
chosen
Gina Matthews is a film and television producer best known for her work on popular romantic comedies such as "13 Going on 30."
-
B.
Gloria Matthews
Gloria Matthews is a central character in Terry McMillan’s novel and its film adaptation "Waiting to Exhale," portrayed as a single mother navigating love, self-worth, and friendship.
-
C.
Tina Clayton
Tina Clayton is a Jamaican sprinter known for her success in international youth and junior sprint competitions, particularly in the 100 meters.
-
D.
Renee Walker
Renee Walker is a fictional FBI agent and counterterrorism analyst from the television series "24," known for her intense dedication and morally complex decisions.
-
E.
Belinda Beatty
Belinda Beatty is known as the wife of the late American character actor Ned Beatty.
- 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_69ca82bcb4848190a9a9d036ad768642 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb43b6b4dc8190be237e6dd21c863b |
completed | March 31, 2026, 3:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce6ca1110c8190b72a2a573ddab06d |
completed | April 2, 2026, 1:18 p.m. |
Created at: March 30, 2026, 5:34 p.m.