Triple
T31626243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Land of the Giants |
E807031
|
entity |
| Predicate | leadActor |
P1507
|
FINISHED |
| Object |
Heather Young
Heather Young is an American actress best known for her role as stewardess Betty Hamilton on the 1960s science fiction television series "Land of the Giants."
|
E1972247
|
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: Heather Young | Statement: [Land of the Giants, leadActor, Heather Young]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Heather Young Triple: [Land of the Giants, leadActor, Heather Young]
Generated description
Heather Young is an American actress best known for her role as stewardess Betty Hamilton on the 1960s science fiction television series "Land of the Giants."
Provenance (5 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_69f348d7883c8190b6c13ab92b7ef076 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6a8e0ebcc8190959911bbf9c977d1 |
completed | May 3, 2026, 1:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2b79d0bf608190a98d96154718ae31 |
completed | June 12, 2026, 3:15 a.m. |
| NEDg | Description generation | batch_6a2b7dd99f7c8190bc9b003895ee91dd |
completed | June 12, 2026, 3:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a2b7e4d24f881908638553543f53f7e |
completed | June 12, 2026, 3:34 a.m. |
Created at: April 30, 2026, 10:43 p.m.