Triple
T12643928
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tammy Grimes |
E301969
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Tammy |
E732794
|
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: Tammy | Statement: [Tammy Grimes, givenName, Tammy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tammy Context triple: [Tammy Grimes, givenName, Tammy]
-
A.
Tammy
Tammy is a resourceful, quirky suburban mom-turned-criminal and a key member of the heist crew in the film "Ocean's 8," portrayed by Sarah Paulson.
-
B.
Tammy
"Tammy" is a popular 1957 ballad closely associated with the film "Tammy and the Bachelor" and widely remembered for its romantic, nostalgic melody.
-
C.
Tammy
Tammy is one of Madea’s outspoken, short-tempered daughters in Tyler Perry’s "Madea’s Big Happy Family," known for her combative attitude and family conflicts.
-
D.
Tammy
chosen
Tammy is a feminine given name commonly used in English-speaking countries, often as a diminutive of names like Tamara or Tamsin.
-
E.
Tammy
"Tammy" is a 2014 American comedy film starring Melissa McCarthy as a down-on-her-luck woman who embarks on a chaotic road trip with her grandmother.
- 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_69d7bdec9f9c8190b4bac675b7588211 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9614bf2f881909976becdf747f4fb |
completed | April 10, 2026, 8:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6687961d88190a605f17425ad7547 |
completed | May 2, 2026, 9:11 p.m. |
Created at: April 9, 2026, 5:17 p.m.