Triple
T27348007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Homo floresiensis |
E684280
|
entity |
| Predicate | estimatedLastAppearanceYearsAgo |
P168082
|
FINISHED |
| Object | 50000 |
—
|
LITERAL 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: 50000 | Statement: [Homo floresiensis, estimatedLastAppearanceYearsAgo, 50000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: estimatedLastAppearanceYearsAgo Context triple: [Homo floresiensis, estimatedLastAppearanceYearsAgo, 50000]
-
A.
lastRegularAppearanceDate
Indicates the date on which an entity made its most recent standard or non-special appearance.
-
B.
yearOfDisappearance
Indicates the specific year in which an entity disappeared or ceased to be present.
-
C.
lastAppearance
Indicates the most recent time or instance in which an entity appears or is present within a given context or sequence.
-
D.
lastSeen
Indicates the most recent time or occasion on which one entity observed, encountered, or had contact with another entity.
-
E.
ageAtDisappearance
Indicates the age an individual was when they disappeared.
- F. None of above. chosen
Provenance (4 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_69ef1480a76481908684256ddd5bfda3 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f673633d288190b52ceb9f8a057c44 |
completed | May 2, 2026, 9:57 p.m. |
| PD | Predicate disambiguation | batch_69f66ec3d3d48190ab2f2b71939e572e |
completed | May 2, 2026, 9:38 p.m. |
| PDg | Predicate description generation | batch_69f67256d064819094be04fc1bbbc635 |
completed | May 2, 2026, 9:53 p.m. |
Created at: April 27, 2026, 11:46 a.m.