Triple
T22495434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Bean |
E556124
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Tom Bean, Texas |
—
|
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: Tom Bean, Texas | Statement: [Tom Bean, associatedWith, Tom Bean, Texas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Bean, Texas Context triple: [Tom Bean, associatedWith, Tom Bean, Texas]
-
A.
Tom Bean, Texas
chosen
Tom Bean, Texas is a small rural city in North Texas located within the Sherman–Denison metropolitan area.
-
B.
Ben Arnold, Texas
Ben Arnold, Texas is a small unincorporated rural community located in Milam County in central Texas.
-
C.
Lamar
Lamar is a surname most notably associated with Mirabeau B. Lamar, the second president of the Republic of Texas.
-
D.
Lamar
Lamar is a small city in southeastern Colorado that serves as an agricultural and transportation hub for the surrounding rural region.
-
E.
Lamar
Lamar is a masculine given name of Old French and Old German origin, commonly used in the United States.
- 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_69e11e5445bc8190b6a9481926db3355 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15cb19bac81909bbc2f131186aeea |
completed | April 29, 2026, 1:19 a.m. |
Created at: April 16, 2026, 8:49 p.m.