Triple
T15909035
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cady Heron |
E385797
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Heron |
E875510
|
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: Heron | Statement: [Cady Heron, familyName, Heron]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Heron Context triple: [Cady Heron, familyName, Heron]
-
A.
Héron
chosen
Héron is a rural municipality in the province of Liège in Wallonia, Belgium, known for its agricultural landscape and small villages.
-
B.
Herculius
Herculius was the honorific title of the Roman emperor Maximian, associating him with the hero-god Hercules as part of Diocletian’s Tetrarchic ideology.
-
C.
Sphinx
The Sphinx is a mythical creature, typically depicted with a lion's body and a human head, known for posing deadly riddles to travelers in Greek mythology.
-
D.
Sphinx
Sphinx is a documentation generation tool that converts reStructuredText (and other formats) into HTML, PDF, and other outputs, widely used for Python projects and technical documentation.
-
E.
Sphinx
Sphinx is a taciturn, highly skilled mechanic and member of the car-stealing crew in the film "Gone in 60 Seconds."
- 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_69d86da686e4819097cbf3b1fc2d881d |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1565d2f048190a40379ceae00411a |
completed | April 16, 2026, 9:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb055307081908a13c98a0e16780c |
completed | May 9, 2026, 10:08 p.m. |
Created at: April 10, 2026, 4:52 a.m.